Importance of Financial Decision-Making in the Business World

In the business world, financial decision-making is important. Some organizations have trouble with accounting and the financial decision-making process in today’s diverse organizational ethics. In this world’s current economy, the expectation for organizations is to behave in an ethical manner. The business world consists of people with different ethical belief systems, which makes it difficult to define ethics (The Journal of Accountancy, 2007). Organizations that do enforce a code of ethics can create unwanted behaviors within the organizations. These unethical behaviors can affect every individual associated with the organization. When an organization instills good ethical behaviors, its rate of success and longevity is more likely to be high. Organizational ethics are a significant part in financial decision-making and accounting. Ethical principles set the foundation on which a cultured society exists. An exceptional illustration of ethics in accounting and finances is the Sarbanes-Oxley Act of 2002. After several financial frauds reported in 2001 and 2002, the president signed the Sarbanes-Oxley Act in July 2002. This act established major modifications to the financial practices and corporate governance regulations. According to n. d. 2006), â€Å"The given name is after Senator Paul Sarbanes and Representative Michael Oxley, who were its main architects, and it also set a number of non-negotiable deadlines for all organizations to comply† (para. 1). The Sarbanes-Oxley Act, known as the corporate responsibility act, act gives considerable supervision responsibilities and control to the Securities an d Exchange Commission (SEC) above organizations external auditors and distribution of financial statements. The SEC must employ a public company accounting oversight board (PCAOB) with the authority to control the public accounting (Albrecht, Stice, Stice, & Swain, 2005, p. 01). This law was put in place because of the involvement of Enron and Tyco International in several accounting scandals. For most organizations, making money is important; money is what makes the business successful. However, a business that becomes greedy and decides to make money illegally will eventually fail. In the article â€Å"Beyond Sarbanes-Oxley†, Neil S. Lebovits, advises businesses to ensure their ethical health by doing several things. Lebovits suggests that organizations must employ the following three ethical best practices to be successful: â€Å"cultivate ethical role models, demonstrate ethical ecision-making, and encourage pushback† (Lebovits, 2006, para. 5). The Directorate of P lanning, Training, Mobilization, and Security (DPTMS) organization that I work for employs these three ethical best practices daily. The management always ensures that workers behave professionally while serving soldiers and making the right decisions that could affect the soldiers training during this time of war. The DPTMS leaders provide explanations on how to make decisions and why the selection of the judgment. The organizational managers have an open-door policy to listen to the workers concerns. Additionally, managers walk throughout the day asking employees if he or she has any issues that the managers could assist on. These types of actions create a sense of trust in the leadership that everyone worker wants to emulate. Lebovits also proposes that organizations can do more to ensure the organization workers behave ethically at all levels. Lebovits suggests that, first, organizations cultivate ethical role models. These role models structures’ must include natural influencers who exhibit strong ethical behavior in their day-to-day work in the financial departments of the organization. Organizations must give influencers proper recognition on every occasion possible. By involving influencers in assignments, the individuals can have an encouraging impact in the organization. When an organization rewards its influencers efforts’, the organization can cultivate ethical conduct (2006). Organizational ethical behavior starts at the top of the organization. The leadership must motivate individuals to follow its behavior. Employees watch and listen to their management carefully for signs of how to act. Leaders should behave accordingly and set the example for the workers to follow. When suitable, leaders must tell workers examples of their business decisions involving their ethics and how they used good judgment. This type of honest conversation provides employees with a quick look on how leaders act and think while representing the organization (2006). Moreover, Lebovits proposes that organizations should also encourage pushback. In other words, companies should encourage their employees to speak up if they question financial actions and decisions that affect them. Ethics hotlines, anonymous e-mails and â€Å"approachable† managers are ways for companies to obtain this type of feedback. Organizations and their key personnel should always conduct themselves ethically and legally. They should promote an environment in which employees can articulate work-related concerns without negative effects and free exchange of information (2006). The organizations that decide to implement and follow good ethical behavior will achieve success and an excellent status as ethical and fair instiution to the clients, employees, and the shareholders. These benefits will assist the organization in the financial phase, and when organizations fail to execute will result in poor financial performance. References Albrecht, Stice, Stice, & Swain, (2005). Accounting: Concepts and Applications (9th Ed. ). Quebecor World, Versailles, KY: South-Western, Thomson. Lebovits, N. (2006, August, 2006). Beyond Sarbanes-Oxley: Three best practices to adopt in your organization. Retrieved March, 2011, from http://www. aicpa. org/pubs/jofa/aug2006/lebovits. htm N. D. (2006). The Sarbanes-Oxley Act. Retrieved from http://www. soxlae. com The Journal of Accountancy (2007). Retrieved March, 2011, from http://www. aicpa. org/pubs/jofa/joahome. htm

Compare and Contrast Man in Water Essay

The man in the water makes what would normally be seen as a normal disaster, if such a thing is possible, into a story that stunned so many people. Allende and Rosenblatt both present alike themes in similar and different ways. One way that these two stories are different is that the man in â€Å"The Man in the Water† tried to help as many people as he could, even when he knew the consequences could be death. But Azucena didn’t really help anyone. Their deaths were also different. 3. Isabel Allende used a realistic fiction story-like approach, while Roger Rosenblatt used an informative, report-like approach. 4. These stories are very similar because they are in the same genre yet still have differences. This essay will compare and contrast these two stories. More specifically, this essay will be comparing and contrasting how both stories convey the genre of magical realism through the characters, and literary devices (how they show it). 5. In the next two paragraphs I will tell you one similarity and one difference that these two writings have regarding their topic and theme. 6. In â€Å"The Man in Water† and â€Å"And of clay are we created†, nature is used in both. They are also both about death, a monster that takes what it wants. Many themes appear in the short stories, but there is only one that both have in common. That is, humans are not powerless against nature. 7. A difference between the short story and the article is that in The Man in the Water, the author didn’t seem to really exaggerate as much as Isabel Allende, the author of And of Clay are we Created. Allende used magic realism and Rosenblatt was more realistic. 8. In two specific stories, Isabel Allende’s And of Clay Are We Created and Roger Rosenblatt’s The Man in the Water, disaster strikes and the people in the stories are symbols of what true human beings should be like; faithful, positive, and strong in the heart and mind. Both†¦

Bedside Shift Report Essay

Policies and procedures are review, revamp and implement constantly in health care facilities to ensure safe patient care is being deliver. Effective communication is a critical part in providing safe patient care. Usefulness communication is essential during shift report in order to provide safe care and meet goals for the patient. There is a trend where hospitals are bringing shift report to the bedside in order to improve the effectiveness of communication between the nurses. At Kaiser Santa Clara, the facility I currently work at, has a standard policy and procedure regarding the handoff communication during shift change, according to the policy the two nurses are to review information that is standardized to the following: †¢Diagnoses and current condition of the patient †¢Medications given or due †¢Isolation status †¢Recent changes in condition or treatment †¢Anticipated changes in condition for treatment †¢What to watch for in the next interval of care The purpose of the policy is to provide an interactive dialogue that allows for up-to-date information on the patient’s care. The policy is referenced to the Joint Commission-mandated focus on improving patient safety through effective caregiver communication. According to the Joint Commission, as estimated 80% of serious medical errors are attributable to miscommunication between caregivers when transferring responsibly for patients (Wakefield, Ragan, Brandt & Tregnago, 2012). Shift report happens two, three, or more times in a day, but nurses receive little formal training in this vital responsibility. Nurses may be found legally liable for failing to report necessary information during handoffs (Riesenberg, Leitzsch, & Cunningham, 2010). Therefore, it is imperative for a handoff procedure incorporate an effective way to communicate in order to provide safe patient care. Review of the Literature Traditionally, shift report has been performed away from the bedside either at the nurse’s station or outside of the patient’s room where patient  information is exchanged in an informal way varying from nurse-to-nurse. According to Laws and Amato, information provided, and the actual status of the patient were two different stories when the on-coming nurse came into the room to assess the patient after shift report (2010). Shift report often lack care planning and goals for the shift; these issues often leave the nurses with incomplete data to provide patients with the best possible care (Baker, 2010). Numerous studies and articles have been written in how to improve shift report to coincide with the Joint Commission national patient safety goals, there seems to be an array of information on facilities transition to bedside report, as in giving shift report right next to the patient’s bed. At the University of Michigan Hospital and Health Center, a quantitative study was conducted to improve the practice of nursing shift-to-shift report by taking it to the bedside. Over a six month period, a group of nurses were observed during shift change to determine how the implementation of bedside reporting was being received by the nurses and patients. The results collected between the observation and a brief questionnaire filled out by the nurses, showed that there was a decrease in report time from 45 minutes to 29 minutes due to that nurses that did not have the privacy of socializing at the nurses station, which decreases crucial time to give a report on a patient. Nurse satisfaction with report process increased from 37% to 78% when moved to the bedside because nurses could give and receive much more accurate handoff without distractions. An intervention to relocate shift report to the patient bedside resulted in improved satisfaction for nurses and increased direct care ti me to patients (Evans, Grunawalt, McClish, Wood, & Friese, 2012). A critical care quality committee at Regions Hospital in St. Paul, Minnesota, was concerned with an audit that showed 39% of medication errors were found after shift report. This evidence supported the development and strength for bedside report. A qualitative study was conducted by surveying the 69 nurses on two different critical care units. The report’s finding indicated improved communication at the bedside along with allowing the nurses to double check on the intravenous medications that were being  administered to the patient. 84.2% of the nurses felt they were more confident about their report when giving it at the bedside because it gave them an opportunity to provide objective information versus subjected information on the patient (Triplett & Schuveiller, 2011). However, through-out the article there was no information regarding if the 39% of medication errors decrease after the implementation of bedside report. There was a mentioned that 55% surveyed did find errors at the bedside during report; however it was not discussed how these errors were addressed. Overall, bedside report has significantly affected nursing practice in a beneficiary way by nursing staff (Triplett & Schuveiller, 2011). In an effort to improve patient satisfaction, an inpatient nursing unit in a Midwest academic health center made a decision to bring shift report at the bedside. A quantitative was conducted by surveying inpatients and 32 nurses on a step-down unit. A yes or no survey was given to the inpatients regarding the quality of the report that was given at the bedside, and 72% were satisfied with the information that was exchanged between the nurses (Wakefield, Ragan, Brandt & Tregnago, 2012). Following the implementation of bedside report there was a significant increase in patient satisfaction scores. While scores improved, transition to the bedside was not well received by nurses. Data collected showed that nurses were not following the new process of bedside report. 60% of the nurses did not do report at the bedside, however decrease by extensive planning, training and gradual implementation (Wakefield, Ragan, Brandt & Tregnago, 2012). The studies strikingly prove that effective communication at the bedside provides safe patient care that has been well received by patients and nurses in most cases. The research proved that bedside report offered several benefits such as an increase in the following: †¢Nurse-to-nurse accountability †¢Patient satisfaction scores †¢Quality of care ratings †¢Patient safety scores (Wakefield, Ragan, Brandt & Tregnago, 2012). Description of the Process There is a considerable amount of information and studies that support bedside reporting. Bedside reporting has shown to increase patient participation and satisfaction, increase nursing teamwork and accountability, and most importantly improve communication between nurses. Kaiser Permanente prides themselves as being innovated in the health-care industry and keeping patients satisfaction scores high. Based on evidence, Kaiser could continue reach their goals by modifying their shift report policy to incorporate bedside report. In order to modify or implement a new policy, the process seems straight forward with Kaiser; there is a protocol that allows the policy to be handled by the appropriate committee group. For changes in handoff communications, I would have to approach the director of patient safety with my recommendations based on evidence, and then this information is turned over to the nursing policy and procedure committee for review, which then is approved by Chief of Nursing or Services. Why bedside report? Sounds simple, but many nurses are set in their ways and may be resistant to this new technique for number reasons. Let it be known, not only does evidence show that bedside report brings patient safety, it always brings ownership and accountably among the staff. Bedside report allows an opportunity for real-time conversations and transfer of trust of patient care in front of the patient. A clinical nurse leader (CNL) would play in a vital role in seeing the implementation goes smoothly among the nurses. A CNL can help the process by making sure the staff is engaged by providing the appropriate knowledge on how the system is going to be implementing, along with the evidence that supports this new change. The key to successfully implementing bedside report is clearly defining the role of the nurses, standardize what is communicated, and allow for time for the patient’s input. A CNL can follow up on the success of the implementation by rounding on the patients and nurses for feedback and  reporting back to nurses with opportunities or wins, which allows the nurses know how they are doing. In conclusion, it has been provided by evidence based information to show that bedside report is a win-win situation for both the nurses and patients and meets the patient safety goals for Joint Commissions.

American Public Law Essay Example | Topics and Well Written Essays - 4000 words

American Public Law - Essay Example The settlement agreement and order cannot prohibit Americans from using travel dates that correspond to travel dates placed by another airline on a published government of military contract. This clause helps airlines from escaping from abiding to contract obligations between two parties and the damage occurred during travel without direct or intentional involvement of the airlines staff or management. As the airlines are not compelling the passenger to travel in their carriers on the date fixed on the ticket, the probability of paying of compensation for injuries caused by tort acts that have no enough witness to prove the involvement of airlines is less. However, one can retain the record of the dates of such travel and the specific fares. 1 In general tort litigation has been blamed for liability insurance to excessive levels. This may reduce real wages and overall employment and thus refrains the administrations from incorporating the compulsion of paying for tort injuries by the carriers or managements of the organisations like airlines and other transport organisations. The contexts of tort contexts are even reducing the willingness of corporations and individuals to pay for even reasonable risks. There is a scant evidence for these claims that are paid for. One more reason for absence of compensation for torts of certain instances is due to the serious harm done for the economy during early 1990s. Though the tort is a wrongful act, damage or injury done wilfully, there is a need to prove the intention of the doer in the absence of substantial evidence like in the cases mentioned for this paper. As the injury is not due to breach of contract, or it is not violation of the circumstances that involve strict liability, though a violation exists, the payment of compensation will be much lesser than that expected by plaintiff or in some cases no need of compensation except for medical and legal costs. One suggestion that can arise from the situation mentioned for this paper is that the administration or airlines may suggest an insurance cover for short term travel or an insurance cover for the passengers travelling with an extra charge of fare. Though the Brainair charges extra than other airlines, the non utilisation of services mentioned for that extra charge do not give any chance for the plaintiff to get compensation for the injury he has been inflicted due to negligence or incorrect operation of the apparatus. In general, there are instances of transferring payments from wrongdoers to victims and to have compensation from this aspect, the plaintiff should find the wrongdoer and prove his claim on it. As it has been mentioned that the fall of suitcase on plaintiff's head is not finding enough e vidence regarding the negligence of staff or malfunctioning of bin, there is little chance to get compensation, until the plaintiff is able to decide the cause for the fall. If it is due to negligence of the staff, the plaintiff can get transfer of payment from the staff of carrier and if it is due to the malfunctioning of the bin, the brainair can be held responsible for paying compensation. Another aspect that is against the paying for compensation is inflated costs in Tort Costs 2004 report in US. As the approach followed by different organisations and candidates is different,

Properties of Materials Essay Example | Topics and Well Written Essays - 1500 words

Properties of Materials - Essay Example According to a survey that was carried out by our firm, we realized that many instances windows were severely damaged. Upon keenly studying the damages, it was realized that it was partly due to environmental factors and partly due to poor maintenance. The damages on the widows caused variations in geometric configuration, composition, porosity and adherence of corrosion products. They also cause environmental pollution and humidity and temperature variations. 2.0 BACKGOUND TO THE STUDY Timber has commonly been used in making window frames, because of its accessibility and ease of processing. It has the lowest thermal conductivity compared to other frame materials. Among the commonly used wood species for window frames are redwood, pine and cedar. Wood can be negatively changed by moisture, which can cause its warping or twisting. This fact makes it a mandatory practice to paint timber windows after a specified duration of time like five years. Another material that has been recently used for window frames is a synthetic material called Polyvinyl chloride (PVC). It is made up of a chain of repeating units of vinyl chloride. PVC comprises of chlorine, carbon, and hydrogen. The PVC exhibit varying characteristics that try to incorporate different additives considered beneficial for the window frames. In order to reduce brittleness, plasticizers may be added as additives in order to improve the processing. This helps to protect against were and tear coursed by natural agents as solar radiation. PVC windows incorporate reinforcement that aids in increasing rigidity. In turn the reinforcement increases the windows thermal conductivity. These windows are suitable and withstand the harsh environment presented by polluted air and saline conditions; their property of high thermal conductivity plays an important role in these hash environments. However, ultraviolet (UV) radiation on PVC breaks its molecular bonds, resulting in increased brittleness (Taylor, 2000). 2.1 Th e likely causes of the damages observed Since every material has its own degradation parameters, the environmental factors affecting the materials, and the intensity of these degradation factors, differ from material to material. For example, timber and PVC can undergo biological attacks but aluminum has no such threats. Some of the likely causes of the damages are: structural movement or stump subsidence in the main frame, expansion of the joints between elements and shrinkage or loose fitting in grooves which causes a problem of rattling windows, moisture penetration, weathering or decay, the normal wear and tear, loose hinges and screws and mold and algae caused by airborne spores, which settle on the surface. It also causes any exterior plastic products to go grey over time. 2.2 Remedial remedies Some of the remedial remedies could be: where the timber has deteriorated and has decayed, the repair could require re-fitting segments of the frame(s) using mortise and tenon joints. C omplete replacement of the damaged sash should also be considered, cracked, split or broken frames in the window sometimes may be repairable using wood adhesives and clamps, rusted and/or ineffective screws on fittings could be replaced, in some cases, by slightly larger diameter screws, by parallel thread metal tread or by longer screws which are non-corroding, one could also consider removing the damaged/rusted area of wood round the

Observation 3 Essay Example | Topics and Well Written Essays - 500 words

Observation 3 - Essay Example The children were supposed to count the digits that appeared on rolling a dice. The activity was meant to check and enhance the counting skills of the students. The teacher had used this activity to achieve this because the children had expressed interest in learning the counting this way a day before. Each of the five students was required to roll the dice on his/her turn three times, and sum up the digits that appeared on rolling the dice each time, so that the final number would be the sum of the digits appearing in three different attempts. The children seemed very happy doing this activity. The teacher organized the activity in the form of groups. This not only provided the teacher with a greater control over the activity, but was also very convenient for the low-achieving students as they had got a chance to work with the high-achieving students. The activity in the form of group was also very useful since there were just 5 teddy bears. Had the teacher decided to conduct the activity in a disintegrated manner or had there been no groups, it would have been hard for the teacher to make sure that every student has had at least one chance of playing with the teddy bear. As the children conducted the activity, the teacher moved over to the groups one after another to extend her hand of help to any group that might be in need of it. The group activity was no less useful for the high-achieving students as they had been provided with a chance to teach others the concepts that they felt hard understanding otherwise. I frequently noticed the high-achieving students helping the others. They felt nice since this was a unique opportunity for them. Last but not the least, the activity taught the children group skills. While the students conducted the math activity in groups, the teacher hovered over them so as to make sure that in each group, each student was participating equally and that all students were

Business Essay Example | Topics and Well Written Essays - 1500 words - 25

Business - Essay Example On the other hand bring it with the organization structure of Hostess and this will depend on the Human Resource department exhibited by the firm, the teams owned by the firm verses the individual behavior of the individual members of the original company. This will go hand in hand with the communication models of the companies and the employee handling skills used by the employees. This may not be actually relevant for the firm since the firm will decide on whether to use distributorship, which operates through contracts. Therefore, the task that is left for this individual is to gunner all the information with regards to the form of distribution they would wish to use and land on the form that is appropriate for Worde white Bread name(See the attachments). A business mode to be employed by a company is quite a formal plan for earning a profit for the company, a business model is otherwise called a profit model and if the right procedure and channel were used in formulating and implementing it then the business would earn a profit out of it (Hoque). This is because the business model employed by for example by the Pepperidge Farm Bread Company would set the bread products and services to be offered to the customers and the way the company will offer such products and services. The distribution model implemented depending on how it is adopted will consider the cost structure and the manner to improve on the sales for the company to bring in more money to widen the gap for profitability while minimizing costs hence expanded profitability. The distribution model if enacted through a good model has always ensured that a wide range of costs as those on employees are negated hence they come in below the sales revenue widening the probability of increasing sales to improve on the profitability. For the distributor model to work as opposed to the employees’ model a series of steps as defined below must be followed in

The Effects of Fertilizer on Plant Growth Research Paper

The Effects of Fertilizer on Plant Growth - Research Paper Example â€Å"A recent assessment found that about 40 to 60% of crop yields are attributable to commercial fertilizer use† (Coleman, Fuenta, and Mock 1978). There are many types of fertilizer out there today for the aid of plant growth. There are many ways to categorize types of fertilizers; they can be classified as either organic or inorganic. It can also be defined as a solid or a liquid fertilizer, along with different ingredients that produce different actions. â€Å"Organic fertilizer is all natural and includes things such as bat guano, compost, peat moss, wood ash and manure. These are general soil amendments. They don't burn or harm plants, and they can have long-term positive effects on the soil without damaging groundwater. Organic fertilizer, however, generally has lower nutrient concentrations than inorganic fertilizers† (Broschat and Moore 2003). Whereas inorganic fertilizer can be classified with the characteristics of. â€Å"Man-made and typically comes as a po wder, pellets, granules or a liquid. Other chemicals that might be included in inorganic fertilizers include calcium, sulfur, iron, zinc and magnesium† (Broschat and Moore 2003). Research Questions 1. What are the specific effects of fertilizer on plant growth? 2. ... Methods Soil was mixed using a 1:1:1 ratio of pet moss, vermiculite, and potting soil. One scoop of the soil was collected and misted with water until it was moist. Four potting trays with six cells each were gathered. The cells were filled half way with the moist soil. Five osmocote pellets were added to each of the cells. The cells were completely filled with the remaining moist soil. A pencil was used to poke three tiny holes in each of the cells. A seed was placed at the top of each hole. The holes were then lightly covered with soil. Each of the cells was labeled one through twelve for the control group and the experimental group. A diamond wick was inserted into each of the cells halfway and was folded over at the bottom. This was to help water enter the cell. Two tubs were filled with water and two pieces of fabric for each tub were soaked and draped over the top of the boxes. Two antiagal tabs were added to each tub of water to prevent algae from growing. One control tray and one experimental tray were placed on each of the tubs. The trays were mixed up so that if something happened to one of the tubs, there would still be a tub that could be examined. The trays were placed under the fluorescent bulbs in the classroom. Plant height, number of leaves, and number of buds were collected each week. Water was added to the tubs weekly as well. Data was analyzed using statistical t-tests. Ethical Considerations Ethics are the moral codes which are followed in a research. These codes are binding and need to be followed irrespective of any circumstance which may surround the research since they give us a remembrance of the researcher’s responsibility towards the people being researched (Chapman and Shaw 2000). The following

MGT Individual Project 5 Research Paper Example | Topics and Well Written Essays - 2000 words

MGT Individual Project 5 - Research Paper Example The study reflects the principles of qualitative and quantitative analysis which are used in business cases. It states that both the methodologies are equally important in conducting the research where a combination of both the methodologies helps in improving the quality of the research work. The study concludes with an insight into the importance of critical thinking in the decision making of the businesses. Table of Contents Abstract 2 Table of Contents 3 Introduction 4 Sekaran & Bougie Research Process 5 Analysis 8 Quantitative Methodology 16 Qualitative Methodology 16 Critical Thinking in Business Cases 17 Conclusion 17 References 19 Introduction Decision making is an important activity in the business world. It is required at all levels of the organization. Even the low level supervisor needs to make various decisions in everyday business activities. Decision making is encouraged at all levels where the business owners are mainly responsible for all the decisions being taken in the businesses. The decision making is mainly a cognitive process which results in choosing of courses of action amongst various alternative scenarios. In each and every decision making process there is a final choice and the output can be the resultant of the opinion or action of such choice. From the cognitive perspective, decision making is mainly a continuous procedure which can be incorporated in interaction with environment. From the normative perspective, it is the analysis of individual decisions associated with the logic in decision making and its rationality. It can be defined as the problem solving activity which gets terminated when satisfactory solution is obtained. The decision making procedure can be rational, irrational, on the basis of tactic assumption or explicit assumption. The business development always arises from collective decision making of the management along with the staff. The programs, strategies and policies are always converted into effective course s of actions by means of proper decision making. The progress in any organization from one particular success and performance level to another arises from the effective decision making. The efficiency in the management of any business enterprise increases along with the application of practical and progressive principles and policies. If these ideas are implemented within the processes of an organization it would result in the increase of efficiency followed by achievement of success in the market. Sekaran & Bougie Research Process According to Sekaran & Bougie (2010) the research process is a step by step process where all the steps are essential for achieving success in the research process. An analysis should always be based on the basis of each and every step presented in this research process. It states that the research analysis should present a comparative analysis of how nicely the authors of the chosen research study have fulfilled the research work. The focus of the analys is should lie in the research process more than on the research content. The analysis should deal with the obedience to the research process stated by Sekaran & Bougie. All the steps and sub steps act as the stepping stones in the research process. The research process is a step by step procedure comprising of eleven steps. While analysing, the research work should have the following contents Problem Statement Literature Review Generation or Development of

Internet Article Review When to call the organization doctor Essay Example for Free

Internet Article Review When to call the organization doctor Essay Summary In the article, When to call the organization doctor by Robert N. Llewellyn discusses many techniques that are available for an organization and managers to use in determining how to properly identify organizational problems, or resolve current problems within the organization. Llewellyns article briefly describes eight-elements in accomplishing organizational effectiveness: Strategic Direction, Goal Alignment, Work Process and Projects, Organizational Structure, Performance Management, Rewards, Cultural Support Systems and Infrastructure. The article further points out that after a manager have identified the elements for effective organizational management than they should apply these elements to diagnosis problems within the organization. Furthermore, When a fit problem is identified one must use not only simple deductive thinking, but inferential thinking as well. (Llewellyn, p.79, 2002) Following this step puts management in charge of where the organization is going, strategically and systemically, and avoids the management-fad phenomenon. (Llewellyn, p.79, 2002) Effective Management In week, one Professor Sowunmi asked the class to explain, How does effective management impact organizational success? (Main newsgroup, February 4, 2004 DQ 2) In answering the question I stated, Effective management can have an endless impact upon the success of an organization. Interview Article 3 The main goals of any business are to make sure that its organization and its employees perform proficiently and productively. Any company can accomplish these goals if the employees are provided with appropriate guidance, enough flexibility, and supplied with the necessary information about what the organization is trying to accomplish. Moreover, a company that is successfully managed has a vision and knows how to make decisions that are consistent with the companys vision. In addition, an organization that has good management can make good decisions that not only improve the profits of the company, but also give the employees a sense of pride in their company. A company that is managed successfully recognizes and appreciates its customers and will go all the way in making sure the customer is place first. (Easter-Brown, DQ 2, February 7, 2004) This statement helps support the fact that if an organization fails to properly diagnosis problems within the organization they are most probably committing a form of organizational malpractice. In other words, thinking about the many ways organizations try to change and make themselves healthier makes it nearly impossible if they are unwilling to remember that prescription without diagnosis is malpractice, whether in medicine or management. On the other hand, the self-medication approach can sometimes have limited impact and can even lose headway. Without any external help or ideas, the side effects or self-treatment can be limited management thinking, stubborn devotion to traditional and comfortable viewpoints. Self-medication can work well, but management must be well informed about the range of effective remedies. Interview Article 4 Take the statement made by Kevin OConnell, one of my fellow classmates, Effective management uses mistakes as opportunities for learning and is able to recover and quickly adapt to changes in the business climate. Ineffective management points the blame on others and never learns nor takes ownership for mistakes. (Main newsgroup DQ2, February 5, 2004) Furthermore, preferring the self-medication approach, many organizations continually engage in various processes aimed at self-change. They may purchase current management books, videos and training materials the counterpart of over-the-counter medications but in general, they prefer to figure things out for themselves. Many firms are simply more comfortable with this do it yourself approach and have little attraction to the idea of bringing in outsiders to deal with their change agenda. Internal task forces, special initiatives, campaigns and focused training programs can be effective forms of self-treatment. If they have a core team of bright, well-qualified internal change agents, they could make great progress. Llewellyn states, managers should first correctly diagnosis organizational problems first, then, if needed, search for a consultant that has the experience needed. (pg. 79, 2002) Changes for SHS If I could make changes or recommendations for my own organization, they would defiantly be built around the guidelines of organizational effectiveness. Interview Article 5 The Stamford Health System is currently under new management and many of the guidelines stated in Llewellyns article are exceptional steps for improving the effectiveness of my companys organizational structure, for example, Performance Management, Rewards, and Cultural Support Systems are some good suggestions for improvement. In my recommendations for Performance Management, I would like to see a centralized scheduling streamlines access to the hospitals services that satisfy physicians and patients, for example, a Centralized-scheduling staff of stationed in close proximity to the Admitting office, increasing their efficiency due to the high volume of walk-in patients. The average 95 faxes and 30 calls received each day from physicians offices will be reduce to approximately 15 minutes to schedule. The training process for central scheduling staff is broader so that they can schedule all procedure/visit types Specific IT systems can now facilitate central scheduling by being smart about scheduling. If my organization wants to be competitive and increase their nurse retention, they need to provide major stimulus to restructure the hospital and organization. There needs to be an improvement in our internal reform strategy as well as a market alliance strategy. In order to be successful in the market environment of managed care and managed competition, my institution needs to expand market share through superior quality service; reduce management overhead with flat structure; increase productivity with self-directed teams; control expenses within budget; reinforce innovation and performance with incentives; and reinfuse employees and medical staff with a sense of shared optimism about the future. Interview Article 6 I believe the result will be a sweeping overhaul in organizational culture, driven by a radical shift in management philosophy and a permanent commitment to seek continuous improvements at all levels. Conclusion It is evident from my evaluation and the article When to call the organization doctor by Robert N. Llewellyn that careful evaluation and diagnosis of the central problems in an organization can help avoid expensive, disruptive, and often unnecessary intervention (pg.79, 2002) Overall, through a conservative position an organization can develop the capability to evaluate and diagnosis effective organizational skills to improve the many problems that may arise in the organizational structure. Fundamentally, as long as the organization is willing to strive for organizational effectiveness, they have a greater chance of solving problems within the company. Interview Article 7 References Easter-Brown, D. (Feb. 7, 2004). MGT 330 Main Newsgroup. How does effective management impact organizational success? Retrieved from MGT 330 Main Newsgroup on February 16, 2004 Llewellyn, R.N. (Mar. 2002). When to call the organization doctor HR Magazine. Vol. 47, Iss. 3, pg. 79. Retrieved from ProQuest database on February 2, 2004. OConnell, K. (Feb. 5, 2004). MGT 330 Main Newsgroup. How does effective management impact organizational success? Retrieved from MGT 330 Main Newsgroup on February 16, 2004 Sowumni, A. (2004). Overview of The Concepts of Management: Week I Lecture. Retrieved from MGT 330 Course Newsgroup on February 13, 2003. University of Phoenix (Ed.). (2002). Management: Theory, Practice, and Application [University of Phoenix Custom Edition e-text]. Upper Saddle River, NJ: Pearson Custom Publishing Retrieved February 13, 2003.

Viable Cell Counting In Yeast Suspension Biology Essay

Viable Cell Counting In Yeast Suspension Biology Essay The aim of this experiment was to estimate the number of viable cells in a yeast suspension that was already provided. Estimations of the viable yeast cells were taken via two methods of plating; pour plating and spread plating, of which hot agar was used with the pour plate technique. The results that were obtained for this experiment show that overall; the spread plate method gives a higher yield of viable yeast cells compared to the total count value of 2.8 x 10^7. This experiment was conducted to estimate the number of viable cells in a yeast suspension, already provided. The definition of a viable cell, as stated in the Collins English Dictionary, 2008, p991 is capable of growth. Therefore, the definition of a viable yeast cell is a yeast cell capable of living and being able to grow. In industrial and research settings, there is a need to quantify the microbe content of microbial products. The method for doing this varies for different types of microbes. Traditionally, the first microbes to be used commercially were bacteria and yeasts. These are typically single-celled species that can be grown in natural and artificial media, and are well-suited to growth in agar gels on Petri plates. Using this method, individual cells or clumps of cells will form discrete colonies, which become visible to the naked eye as the colony grows. Counting the number of colonies provides a direct way to track the original number of discrete microbial units. A count determined this way been dubbed the number of Colony-Forming Units or CFU for short. CFUs are only applicable to single-celled microbes that can be grown on nutrient media, such as bacteria, yeasts, or spore-forming moulds. As the total count for the number of yeast cells was so vast (2.8 x 10^7) dilutions were made in order for a characteristic estimate of the total count of yeast cells to be made. Having diluted the sample enabled the human eye to count an estimate of the yeast cells. If dilutions had not been carried out, the sample of yeast cells would have been far too large and it would have been extremely time consuming and impossible to count the number of yeast cells. A haemocytometer enables for an estimate of the total number of yeast cells present. It has a known volume of chamber and area which is etched on the glass. A cell suspension is able to be above the known area. The chamber is then filled with a yeast suspension then covered with a cover slip. An average number of microbes can then be counted in the ruled area to give the number of yeast cells per cm ³. The aim of this experiment was essentially, to estimate the total number of yeast cells in a culture and to estimate the number of living (viable) yeast cells. Materials and Methods The total count of the yeast cells originally estimated by the haemocytometer was 3.8 x 10^7, however, it was later concluded that this was incorrect due to a mix up from another class. The new result for the estimated total count of the yeast cells was 2.8 x 10^7. This number was clearly too large and a series of ten fold dilutions were carried out in order to make it easier to estimate and investigate the viable yeast cells. A series of ten fold dilutions were needed as this is an important technique in identifying the viable cells. As a figure of 2.8 x 10^7 was established and it is vital that the number of colonies attained remains within the range of 30-300. So the dilution for a range of 30 300 is 1/100 (10^-2), however, it is essential that further dilutions, both above and below 1/100 are used; 1/10 (10^-1) and 1/1000 (10^-3). To make the estimation more accurate, dilutions of 10^-4, 10^-5 and 10^-6 were also used for both pour and spread plates. For full method, please refer to introduction to biology, microbiology and pharmacology practical booklet, pp 13-14. Results The results obtained for the pour plate and spread plate methods were as follow: 10^-1 10^-2 10^-3 10^-4 10^-5 10^-6 Pour plate (ml) TNTC TNTC TNTC TNTC 83 8 Spread plate (ml) TNTC TNTC TNTC 100 10 1 Key: TNTC Too numerous to count To determine the number of colony forming units (CFU) cm^-3 this calculation was used: Counts on plate x (1/dilution) x (1/volume inoculated (ml)) The calculations that were carried in order to determine the number of colony forming units (CFU) cm^-3 of the original culture for the pour plate and spread plate are shown below: Calculations for pour plate method: 83 x 1/10^-5 a 1/1 = 8.3 x 10^6 CFU, ml Calculations for spread plate method: 100 x 1/10^-4 x 1/0.1 = 1.0 x 10^7 CFU, ml The volumes inoculated for the pour and spread plate were different, the pour plate was inoculated with 1.0cm^-3 and the spread plate with 0.1micrometer. Discussion The table in the results sections shows that the values of the colonies that were counted for each of the plating techniques show good continuation, especially with the spread plate as the figures are increasing by a factor of ten each time. The figure obtained for the total count was 2.8 x 10^7, comparing this to the figure calculated for the pour plate method, 8.3 x 10^6 CFU, ml there has been a loss in the number of viable cells using this method, there has been a decrease of 1.97 x 10^7 of viable yeast cells. Comparing the total count value to the spread plate figure of 1.0 x 10^7 there was also a loss of viable yeast cells, with a loss of 1.8 x 10^7. This decrease in viable yeast cells compared to the pour plate loss is lower. The hot agar used in the pour plate technique may injure or kill sensitive cells; thus spread plates sometimes give higher counts than pour plates.(p 130, Microbiology, Seventh Edition, Joanne M. Willey et al) The above statement backs up the results of the experiment, as the spread plate technique has given a considerable higher count of viable yeast cells. Other factors that may have resulted in the smaller number of viable yeast cells in the pour plate method could have been that there is a much higher likelihood that clumps of the colonies may have formed together in portions of the plate, making it much more difficult to count. This occurs less in spread plating, as the clumps are broken up, and therefore there is a better distribution of the cells. Other factors that may have affected the results obtained for this experiment were the techniques used for the serial dilutions. With each sequential serial dilution step, there may have been transfer inaccuracies that lead to less accurate and less precise dispensing. This meant that the highest dilutions had the highest number of inaccuracies. Also, after every inoculation, the dilution must be thoroughly mixed; this was not carried during any of the dilutions, so this may have also affected the number of viable yeast cells. Finally, when doing viable counts, the higher dilution is, the more error is found in estimating the count of the original volume. For example, there were 10 colonies growing on the 10^-5 spread plate, and it was estimated that there were approximately 500000 colonies in the original suspension, but this was only an estimation to the closest hundred thousand. Likewise, with higher dilutions, such as the 10^6 on the pour plate, it was only estimated to the closest million. There were some limitations to the experiment, which may have altered the results slightly. Not having much experience in using the Gilson pipettes may have had an impact on the accuracy of the pipetting that was done during the serial dilutions.

The Australian Legal Systems

The Australian Legal Systems The Australian legal system is based on a fundamental belief in the rule of law, justice and the independence of the judiciary. All people of Australia and non-Australians are treated equally before the law and safeguards exist to ensure that people are not treated arbitrarily or unfairly by governments or officials. Principles such as procedural fairness, judicial precedent and the separation of powers are fundamental to Australias legal system. The common law system, as developed in the United Kingdom, forms the basis of Australian jurisprudence. It is distinct from the civil law systems that operate in Europe, South America and Japan, which are derived from Roman law. Other countries that employ variations of the common law system are the United States, Canada, New Zealand, Malaysia and India. The chief feature of the common law system is that judges decisions in pending cases are informed by the decisions of previously settled cases. Consitution of Australia The United Kingdom passed the Commonwealth of Australian Constitution Act 1900. The significant of the Act was that it created a federal Commonwealth compraising the Commonwealth of Australia and the states. It also incorporated the constitution which came in to effect on January 1901. The Australian Constitution of 1901 established a federal system of government, under which powers are distributed between the federal government and the states Itdefined exclusive powers (investing the federal government with the exclusive power to make laws on matters such as trade and commerce, taxation, defence, external affairs, and immigration and citizenship) and concurrent powers (where both tiers of government are able to enact laws). Thestates and territories have independent legislative power in all matters not specifically assigned to the federal government. Where there is any inconsistency between federal and state or territory laws, federal laws prevail. Federal laws apply to the whole of Australia. Seperation of powers Governing Australia needs lots of power. The Constitution says that this power is divided between three groups of people so they can balance each other. Each group checks the power of the other two. This division of power stops one person or group of people taking over all the power to govern Australia. Legislative power means the power to make laws and is concentrated in the Parliament. Executive power means the power to implement laws and is given to the government. Judicial power gives the High Court power to decide whether laws are legal according to the Constitution. Division of Powers The law making powers which are not stated in the constitution as belonging to the commonwealth remains with the state .The powers are divided between the State Parliament and the Commonwealth parliament.There are some areas where both the commonwealth and the states have power to make laws these are concurrent powers,for example ,the taxation power. The state can however be excluded from these areas if their law are in consistant with those of the commonwealth. Some powers are stated to be exclusive to common wealth. These includes defence powers , the power to impose exercise and customs dudies , the currency, coin age and legal tender power and making of law for the government of a territory. The commonwealth is irestricted on areas for which it can make laws, the state can make laws on the commonwealth areas as long as they are with in the juristiction of the state,where a commonwealth has not been specifically given a power to legislate, then those remaining powers are exclusive to the states , for instance motor law , Criminal law and contract law. Most business law are made as state laws The Commonwealth Parliament The Parliament is at the very heart of the Australian national government. The Parliament consists of the Queen ,represented by the Governor General and two Houses (the Senate and the House of Representatives). These three elements make Australia a constitutional monarchy and parliamentary democracy. There are five important functions of parliament: to provide for the formation of a government; to legislate; to provide the funds needed for government; to provide a forum for popular representation; and to scrutinise the actions of government. The Governor-General The Governor-General is appointed by the Queen on the advice of the Prime Minister. The Governor-General performs a large number of functions which are defined by the Constitution, but fall roughly into three categories: constitutional and statutory duties, formal ceremonial duties, and non-ceremonial social duties. On virtually all matters, however, the Governor-General acts on the advice of the Ministry. The Senate The Senate has 76 Senators 12 are elected for each of the 6 states, and 2 each for the Australian Capital Territory and the Northern Territory. State Senators are elected for 6 year terms, territory Senators for 3 year terms. Historically, the Senate has been regarded as a States House: the States enjoy equal representation in the Senate, regardless of their population, and State matters are still important to Senators. The modern Senate is a very powerful Chamber. Bills cannot become law unless they are agreed to in the same terms by each House, except in the rare circumstances of a double dissolution followed by a joint sitting of both the houses The Senate has a highly developed committee system and Senators spend much of their time on committee work. The House of Representatives The House of Representatives has 150 Members each representing a separate electoral division. Members are elected for terms of up to 3 years. The most distinctive feature of the House is that the party or group with majority support in the House forms the Government. The accountability of the Government is illustrated every sitting day, especially during Question Time. Members have many other functions. They are involved in law making, committee work and in representing their electors. Executive Government The Prime Minister is appointed by the Governor-General, who by convention under the Constitution, must appoint the parliamentary leader of the party, or coalition of parties, which has a majority of seats in the House of Representatives. This majority party becomes the government and provides the ministers, all of whom must be members of Parliament. The Federal Executive Council, referred to in the Constitution, comprises all ministers, with the Governor-General presiding. Its principal functions are to receive ministerial advice and approve the signing of formal documents such as proclamations, regulations, ordinances and statutory appointments. Federal Judicature The Constitution provides for the establishment of the High Court of Australia and such other courts as Parliament may create. The judges of the High Court are appointed by the Governor-General in Council (acting on advice of the Federal Executive Council). The functions of the High Court are to interpret and apply the law of Australia; to decide cases of special federal significance including challenges to the constitutional validity of laws; and to hear appeals, by special leave, from Federal, State and Territory courts State and territory courts. Australian state and territory courts have jurisdiction in all matters brought under state or territory laws. They also handle some matters arising under federal laws, where jurisdiction has been conferred by the federal parliament. State and territory courts deal with most criminal matters, whether arising under federal, state or territory law.Each state and territory court system operates independently.

John And The Rebels: Act V Of Tragedy Of Richard Iii Rewritten As A Na :: essays research papers

The boy-page held the tent flap open as Richmond and his officers emerged out. They had been occupied in there since the messenger came with the letter from Stanley and had not emerged for hours afterwards. The page had waited obediently; making sure that no one interrupted the counsel. As Richmond came out, his kind eyes fell on the boy and he greeted him with a warm smile, â€Å"Hello John†. He remembers my name! John’s heart filled with pleasure. His nervousness didn’t let him speak so he just bowed and smiled back. Richmond ruffled his blond hair and asked him to tend to his duties. Since the page had none, he just moved away and watched the knight pass through the ranks, instructing and encouraging the men to get ready for tomorrow’s battle. John knew that he had made the right decision when he fled from London to join the rebels. Since he was only eleven he was not allowed to be a soldier but Sir Oxford had noticed the boy’s skill with horses and so he had personally taken him in to be his page. He was content with the time spent in Richmond’s force although it was filled with hard chores. He was made responsible for many things and this made him proud to be a pageboy. He had met many other boys like him, who had fled from the tyranny of the evil King. Like the others his own family had suffered under Richard’s harsh rule. The Kings men had beheaded his father, being a noble. His mother had then left for another man and had forgotten about her only child. No one else to turn to, John fled to Richmond. Here he found the love and protection he had yearned for and enjoyed the hard work found in a marching army. A cheer from the men caused John to interrupt his thoughts. He saw that Richmond now stood on a platform, his head high above the others. John looked at him with admiration and pride. The knight’s warm eyes surveyed his men in a way that filled them with courage and security. As he spoke his bold voice carried clearly to John, â€Å"My brave men and loving friends! We have marched into the centre of the land with no resistance from the enemy. Victory is near. Our forces shall crush the tyranny that plagues this land.

Tupac :: essays research papers

Tupac Shakur was one of the most influential music artist of the 20th Century. â€Å"Murda, Murda, Murda, and Kill, Kill, Kill†¦Ã¢â‚¬  these are they lyrics to one of the songs written by Tupac Shakur. Amidst all the controversy surrounding his personal life, this artist has managed to overcome all obstacles and spread his hope/hate message to a surprisingly receptive audience. Tupac’s music is borrowed from the styles of early rap and hip-hop yet its appeal rested in Tupac himself. His persona of â€Å"Thug Poet† opened up a portal into the new genre of â€Å"Gangsta Rap.† This new style of music revolutionized the music industry and allowed several new artists to break through in Tupac’s creation, Gangsta Rap, such as; G-unit, Eminem, and many others.   Ã‚  Ã‚  Ã‚  Ã‚  Tupac Amaru Shakur was born on June 16, 1971 in New York City to Afeni Shakur, a Black Panther member since 1968. She gave birth to him 2 months after she was released from Women’s House of Detentions in Grenwich Village. She was charged with conspiracy to bomb several New York public locations and just had her bail revoked. In court she represented herself and won against the state of New York in a surprising turn out. In Incan dialect, his name Tupac Amaru means â€Å"shining serpent† and Shakur is Arabic for â€Å"thankful to God.† For most of his childhood his crack addicted mother shuffled Tupac between the ghettos of Harlem and the Bronx. Young Tupac began his performance career with the 127th Street Ensemble and then enrolled Baltimore School for the Arts where he was educated in ballet and acting. Tupac was forced to drop out of the school because he had to move to California with his mother, where his criminal career began. He left hi s house at the age of 17 because of the continuous fights with his mother he then began selling/doing drugs, and was homeless for about 2 years. His life was spiraling down wards at a rapid rate. Till one day he got his big break. Tupac always dreamt about being famous someday, now his dream was becoming a reality. He struck a recording deal with Interscope records. He was on his way to super stardom, but as we all know with fame comes problems. He was involved in the shooting of two off duty police officers, although the chargers were later dropped. He was also convicted of rape, and sentenced to 5 years in Clinton Correctional Facilities.

Attendance System

Student Attendance System Based On Fingerprint Recognition and One-to-Many Matching A thesis submitted in partial ful? llment of the requirements for the degree of Bachelor of Computer Application in Computer Science by Sachin (Roll no. 107cs016) and Arun Sharma (Roll no. 107cs015) Under the guidance of : Prof. R. C. Tripathi Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela-769 008, Orissa, India 2 . Dedicated to Our Parents and Indian Scienti? c Community . 3 National Institute of Technology Rourkela Certi? cateThis is to certify that the project entitled, ‘Student Attendance System Based On Fingerprint Recognition and One-to-Many Matching’ submitted by Rishabh Mishra and Prashant Trivedi is an authentic work carried out by them under my supervision and guidance for the partial ful? llment of the requirements for the award of Bachelor of Technology Degree in Computer Science and Engineering at National Institute of Techno logy, Rourkela. To the best of my knowledge, the matter embodied in the project has not been submitted to any other University / Institute for the award of any Degree or Diploma.Date – 9/5/2011 Rourkela (Prof. B. Majhi) Dept. of Computer Science and Engineering 4 Abstract Our project aims at designing an student attendance system which could e? ectively manage attendance of students at institutes like NIT Rourkela. Attendance is marked after student identi? cation. For student identi? cation, a ? ngerprint recognition based identi? cation system is used. Fingerprints are considered to be the best and fastest method for biometric identi? cation. They are secure to use, unique for every person and does not change in one’s lifetime. Fingerprint recognition is a mature ? ld today, but still identifying individual from a set of enrolled ? ngerprints is a time taking process. It was our responsibility to improve the ? ngerprint identi? cation system for implementation on lar ge databases e. g. of an institute or a country etc. In this project, many new algorithms have been used e. g. gender estimation, key based one to many matching, removing boundary minutiae. Using these new algorithms, we have developed an identi? cation system which is faster in implementation than any other available today in the market. Although we are using this ? ngerprint identi? cation system for student identi? ation purpose in our project, the matching results are so good that it could perform very well on large databases like that of a country like India (MNIC Project). This system was implemented in Matlab10, Intel Core2Duo processor and comparison of our one to many identi? cation was done with existing identi? cation technique i. e. one to one identi? cation on same platform. Our matching technique runs in O(n+N) time as compared to the existing O(Nn2 ). The ? ngerprint identi? cation system was tested on FVC2004 and Veri? nger databases. 5 Acknowledgments We express our profound gratitude and indebtedness to Prof. B.Majhi, Department of Computer Science and Engineering, NIT, Rourkela for introducing the present topic and for their inspiring intellectual guidance, constructive criticism and valuable suggestion throughout the project work. We are also thankful to Prof. Pankaj Kumar Sa , Ms. Hunny Mehrotra and other sta? s in Department of Computer Science and Engineering for motivating us in improving the algorithms. Finally we would like to thank our parents for their support and permitting us stay for more days to complete this project. Date – 9/5/2011 Rourkela Rishabh Mishra Prashant Trivedi Contents 1 Introduction 1. 1 1. 2 1. 3 1. 4 1. 1. 6 1. 7 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Motivation and Challenges . . . . . . . . . . . . . . . . . . . . . . . . Using Biometrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . What is ? ngerprint? . . . . . . . . . . . . . . . . . . . . . . . . . . . Why use ? ngerprints? . . . . . . . . . . . . . . . . . . . . . . . . . . . Using ? ngerprint recognition system for attendance management . . . Organization of the thesis . . . . . . . . . . . . . . . . . . . . . . . . 17 17 17 18 18 19 19 19 21 21 22 23 24 24 30 30 33 33 33 35 35 36 36 2 Attendance Management Framework 2. 2. 2 2. 3 2. 4 2. 5 Hardware – Software Level Design . . . . . . . . . . . . . . . . . . . . Attendance Management Approach . . . . . . . . . . . . . . . . . . . On-Line Attendance Report Generation . . . . . . . . . . . . . . . . . Network and Database Management . . . . . . . . . . . . . . . . . . Using wireless network instead of LAN and bringing portability . . . 2. 5. 1 2. 6 Using Portable Device . . . . . . . . . . . . . . . . . . . . . . Comparison with other student attendance systems . . . . . . . . . . 3 Fingerprint Identi? cation System 3. 1 3. 2 How Fingerprint Recognition works? . . . . . . . . . . . . . . . . . Fingerprint Identi? cation Sys tem Flowchart . . . . . . . . . . . . . . 4 Fingerprint Enhancement 4. 1 4. 2 4. 3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 6 CONTENTS 4. 4 4. 5 4. 6 4. 7 Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . . . . . Gabor ? lter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 38 39 40 40 41 41 42 42 43 44 45 45 45 46 47 47 50 51 53 53 54 54 55 56 57 59 59 59 59 60 5 Feature Extraction 5. 1 5. 2 Finding the Reference Point . . . . . . . . . . . . . . . . . . . . . . . Minutiae Extraction and Post-Processing . . . . . . . . . . . . . . . . 5. 2. 1 5. 2. 2 5. 2. 3 5. 3 Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . Post-Processing . . . . . . . . . . . . . . . . . . . . . . . . . Removing Boundary Minutiae . . . . . . . . . . . . . . . . . . Extraction of the key . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 3. 1 What is key? . . . . . . . . . . . . . . . . . . . . . . . . . . Simple Key . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complex Key . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Partitioning of Database 6. 1 6. 2 6. 3 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation of Fingerprint . . . . . . . . . . . . . . . . . . . . . . . Partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Matching 7. 1 7. 2 7. 3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Existing Matching Techniques . . . . . . . . . . . . . . . . . . . . . One to Many matching . . . . . . . . . . . . . . . . . . . . . . . . . . 7. 3. 1 7. 4 7. 5 Method of One to Many Matching . . . . . . . . . . . . . . . Performing key match and full matching . . . . . . . . . . . . . . . . Time Complexity of this matching technique . . . . . . . . . . . . . . 8 Experimental Analysis 8. 1 8. 2 Implementation Environment . . . . . . . . . . . . . . . . . . . . . . Fingerprint Enhancement . . . . . . . . . . . . . . . . . . . . . . . . 8. 2. 1 8. 2. 2 Segmentation and Normalization . . . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . 8 8. 2. 3 8. 2. 4 8. . 5 8. 3 CONTENTS Ridge Frequency Estimation . . . . . . . . . . . . . . . . . . . Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . Binarisation and Thinning . . . . . . . . . . . . . . . . . . . . 60 60 61 62 62 62 63 64 64 64 64 65 66 66 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 3. 1 Minutiae Extraction and Post Processing . . . . . . . . . . . . Minutiae Extraction . . . . . . . . . . . . . . . . . . . . . . . After Removing Spuriou s and Boundary Minutiae . . . . . . . 8. 3. 2 Reference Point Detection . . . . . . . . . . . . . . . . . . . . 8. 4 Gender Estimation and Classi? ation . . . . . . . . . . . . . . . . . . 8. 4. 1 8. 4. 2 Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . Classi? cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 5 8. 6 Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 6. 1 8. 6. 2 Fingerprint Veri? cation Results . . . . . . . . . . . . . . . . . Identi? cation Results and Comparison with Other Matching techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 70 73 74 75 75 79 8. 7 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Conclusion 9. 1 Outcomes of this Project . . . . . . . . . . . . . . . . . . . . . . . . . 10 Future Work and Expectations 10. 1 Approach for Future Work A Matlab functions . . . . . . . . . . . . . . . . . . . . . . . List of Figures 1. 1 2. 1 2. 2 2. 3 2. 4 2. 5 2. 6 2. 7 2. 8 3. 1 4. 1 4. 2 Example of a ridge ending and a bifurcation . . . . . . . . . . . . . . Hardware present in classrooms . . . . . . . . . . . . . . . . . . . . . Classroom Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ER Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 0 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 1 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Level 2 DFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Portable Device . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Identi? cation System Flowchart . . . . . . . . . . . . . . Orientation Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinne d Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 1 Row 1: ? lter response c1k , k = 3, 2, and 1. Row 2: ? lter response c2k , k = 3, 2, and 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. 2 5. 3 Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 42 43 40 18 22 23 25 26 27 27 28 29 34 37 Examples of typical false minutiae structures : (a)Spur, (b)Hole, (c)Triangle, (d)Spike . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 44 44 45 48 5. 4 5. 5 5. 6 6. 1 Skeleton of window centered at boundary minutiae . . . . . . . . . . Matrix Representation of boundary minutiae . . . . . . . . . . . . . Key Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 10 6. 2 6. 3 LIST OF FIGURES 135o blocks of a ? ngerprint . . . . . . . . . . . . . . . . . . . . . . . . Fingerprint Classes (a)Left Loop, (b)Right Loop, (c)Whorl, (d 1)Arch, (d2)Tented Arch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. 4 7. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 8. 9 Partitioning Database . . . . . . . . . . . . . . . . . . . . . . . . . . One to Many Matching . . . . . . . . . . . . . . . . . . . . . . . . . . Normalized Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . Orientation Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ridge Frequency Image . . . . . . . . . . . . . . . . . . . . . . . . . . Left-Original Image, Right-Enhanced Image . . . . . . . . . . . . . . Binarised Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thinned Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . All Extracted Minutiae . . . . . . . . . . . . . . . . . . . . . . . . . . Composite Image with spurious and boundary minutiae . . . . . . . . Minutiae Image after post-processing . . . . . . . . . . . . . . . . . 51 52 57 59 60 60 61 61 62 62 63 63 64 65 50 8. 10 Compo site Image after post-processing . . . . . . . . . . . . . . . . . 8. 11 Plotted Minutiae with Reference Point(Black Spot) . . . . . . . . . . 8. 12 Graph: Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . 8. 13 Graph: Time taken for Identi? cation vs Size of Database (n2 identi? cation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. 14 Expected Graph for comparison : Time taken for Identi? cation vs Size of Database(1 million) . . . . . . . . . . . . . . . . . . . . . . . . . 68 69 71 List of Tables 2. 1 5. 1 8. 1 8. 2 8. 3 8. 4 8. 5 8. 6 8. 7 8. 8 Estimated Budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Properties of Crossing Number . . . . . . . . . . . . . . . . . . . . . 22 43 64 65 66 66 67 67 68 Average Number of Minutiae before and after post-processing . . . . Ridge Density Calculation Results . . . . . . . . . . . . . . . . . . . . Classi? catio n Results on Original Image . . . . . . . . . . . . . . . . Classi? cation Results on Enhanced Image . . . . . . . . . . . . . . . Time taken for Classi? cation . . . . . . . . . . . . . . . . . . . . . . .Time taken for Enrolling . . . . . . . . . . . . . . . . . . . . . . . . . Error Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Performance of ours and n2 matching based identi? cation techniques on a database of size 150 . . . . . . . . . . . . . . . . . . . . . . . . . 70 11 List of Algorithms 1 2 3 4 Key Extraction Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . Gender Estimation Algorithm . . . . . . . . . . . . . . . . . . . . . . . Key Based One to Many Matching Algorithm . . . . . . . . . . . . . . Matching Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 49 55 56 12Chapter 1 Introduction 1. 1 Problem Statement Designing a student attendance management system based on ? ngerprint recognition and faster one to many ident i? cation that manages records for attendance in institutes like NIT Rourkela. 1. 2 Motivation and Challenges Every organization whether it be an educational institution or business organization, it has to maintain a proper record of attendance of students or employees for e? ective functioning of organization. Designing a better attendance management system for students so that records be maintained with ease and accuracy was an important key behind motivating this project.This would improve accuracy of attendance records because it will remove all the hassles of roll calling and will save valuable time of the students as well as teachers. Image processing and ? ngerprint recognition are very advanced today in terms of technology. It was our responsibility to improve ? ngerprint identi? cation system. We decreased matching time by partitioning the database to one-tenth and improved matching using key based one to many matching. 13 14 CHAPTER 1. INTRODUCTION 1. 3 Using Biometrics Bi ometric Identi? cation Systems are widely used for unique identi? cation of humans mainly for veri? cation and identi? ation. Biometrics is used as a form of identity access management and access control. So use of biometrics in student attendance management system is a secure approach. There are many types of biometric systems like ? ngerprint recognition, face recognition, voice recognition, iris recognition, palm recognition etc. In this project, we used ? ngerprint recognition system. 1. 4 What is ? ngerprint? A ? ngerprint is the pattern of ridges and valleys on the surface of a ? ngertip. The endpoints and crossing points of ridges are called minutiae. It is a widely accepted assumption that the minutiae pattern of each ? ger is unique and does not change during one’s life. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction. Figure 1 illustrates an example of a ridge en ding and a bifurcation. In this example, the black pixels correspond to the ridges, and the white pixels correspond to the valleys. Figure 1. 1: Example of a ridge ending and a bifurcation When human ? ngerprint experts determine if two ? ngerprints are from the same ? nger, the matching degree between two minutiae pattern is one of the most important factors.Thanks to the similarity to the way of human ? ngerprint experts and compactness of templates, the minutiae-based matching method is the most widely studied matching method. 1. 5. WHY USE FINGERPRINTS? 15 1. 5 Why use ? ngerprints? Fingerprints are considered to be the best and fastest method for biometric identi? cation. They are secure to use, unique for every person and does not change in one’s lifetime. Besides these, implementation of ? ngerprint recognition system is cheap, easy and accurate up to satis? ability. Fingerprint recognition has been widely used in both forensic and civilian applications.Compared with o ther biometrics features , ? ngerprint-based biometrics is the most proven technique and has the largest market shares . Not only it is faster than other techniques but also the energy consumption by such systems is too less. 1. 6 Using ? ngerprint recognition system for attendance management Managing attendance records of students of an institute is a tedious task. It consumes time and paper both. To make all the attendance related work automatic and on-line, we have designed an attendance management system which could be implemented in NIT Rourkela.It uses a ? ngerprint identi? cation system developed in this project. This ? ngerprint identi? cation system uses existing as well as new techniques in ? ngerprint recognition and matching. A new one to many matching algorithm for large databases has been introduced in this identi? cation system. 1. 7 Organization of the thesis This thesis has been organized into ten chapters. Chapter 1 introduces with our project. Chapter 2 explains t he proposed design of attendance management system. Chapter 3 explains the ? ngerprint identi? cation system used in this project.Chapter 4 explains enhancement techniques, Chapter 5 explains feature extraction methods, Chapter 6 explains our database partitioning approach . Chapter 7 explains matching technique. Chapter 8 explains experimental work done and performance analysis. Chapter 9 includes conclusions and Chapter 10 introduces proposed future work. Chapter 2 Attendance Management Framework Manual attendance taking and report generation has its limitations. It is well enough for 30-60 students but when it comes to taking attendance of students large in number, it is di? cult. For taking attendance for a lecture, a conference, etc. oll calling and manual attendance system is a failure. Time waste over responses of students, waste of paper etc. are the disadvantages of manual attendance system. Moreover, the attendance report is also not generated on time. Attendance report wh ich is circulated over NITR webmail is two months old. To overcome these non-optimal situations, it is necessary that we should use an automatic on-line attendance management system. So we present an implementable attendance management framework. Student attendance system framework is divided into three parts : Hardware/Software Design, Attendance Management Approach and On-line Report Generation.Each of these is explained below. 2. 1 Hardware – Software Level Design Required hardware used should be easy to maintain, implement and easily available. Proposed hardware consists following parts: (1)Fingerprint Scanner, (2)LCD/Display Module (optional), (3)Computer 16 2. 2. ATTENDANCE MANAGEMENT APPROACH Table 2. 1: Estimated Budget Device Cost of Number of Total Name One Unit Units Required Unit Budget Scanner 500 100 50000 PC 21000 100 2100000 Total 21,50,000 (4)LAN connection 17 Fingerprint scanner will be used to input ? ngerprint of teachers/students into the computer softwar e.LCD display will be displaying rolls of those whose attendance is marked. Computer Software will be interfacing ? ngerprint scanner and LCD and will be connected to the network. It will input ? ngerprint, will process it and extract features for matching. After matching, it will update database attendance records of the students. Figure 2. 1: Hardware present in classrooms Estimated Budget Estimated cost of the hardware for implementation of this system is shown in the table 2. 1. Total number of classrooms in NIT Rourkela is around 100. So number of units required will be 100. 2. 2 Attendance Management ApproachThis part explains how students and teachers will use this attendance management system. Following points will make sure that attendance is marked correctly, without any problem: (1)All the hardware will be inside classroom. So outside interference will be absent. (2)To remove unauthorized access and unwanted attempt to corrupt the hardware by students, all the hardware ex cept ? ngerprint scanner could be put inside a small 18 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK cabin. As an alternate solution, we can install CCTV cameras to prevent unprivileged activities. (3)When teacher enters the classroom, the attendance marking will start.Computer software will start the process after inputting ? ngerprint of teacher. It will ? nd the Subject ID, and Current Semester using the ID of the teacher or could be set manually on the software. If teacher doesn’t enter classroom, attendance marking will not start. (4)After some time, say 20 minutes of this process, no attendance will be given because of late entrance. This time period can be increased or decreased as per requirements. Figure 2. 2: Classroom Scenario 2. 3 On-Line Attendance Report Generation Database for attendance would be a table having following ? elds as a combination for primary ? ld: (1)Day,(2)Roll,(3)Subject and following non-primary ? elds: (1)Attendance,(2)Semester. Using this tabl e, all the attendance can be managed for a student. For on-line report generation, a simple website can be hosted on NIT Rourkela servers, 2. 4. NETWORK AND DATABASE MANAGEMENT 19 which will access this table for showing attendance of students. The sql queries will be used for report generation. Following query will give total numbers of classes held in subject CS423: SELECT COUNT(DISTINCT Day) FROM AttendanceTable WHERE SUBJECT = CS423 AND Attendance = 1 For attendance of oll 107CS016, against this subject, following query will be used: SELECT COUNT(Day) FROM AttendanceTable WHERE Roll = 107CS016 AND SUBJECT = CS423 AND Attendance = 1 Now the attendance percent can easily be calculated : ClassesAttended ? 100 ClassesHeld Attendance = (2. 1) 2. 4 Network and Database Management This attendance system will be spread over a wide network from classrooms via intranet to internet. Network diagram is shown in ? g. 2. 3. Using this network, attendance reports will be made available over in ternet and e-mail. A monthly report will be sent to each student via email and website will show the updated attendance.Entity relationship diagram for database of students and attendance records is shown in ? g. 2. 4. In ER diagram, primary ? elds are Roll, Date, SubjectID and TeacherID. Four tables are Student, Attendance, Subject and Teacher. Using this database, attendance could easily be maintained for students. Data? ow is shown in data ? ow diagrams (DFD) shown in ? gures 2. 5, 2. 6 and 2. 7. 2. 5 Using wireless network instead of LAN and bringing portability We are using LAN for communication among servers and hardwares in the classrooms. We can instead use wireless LAN with portable devices.Portable device will have an embedded ? ngerprint scanner, wireless connection, a microprocessor loaded with a software, memory and a display terminal, see ? gure 2. 5. Size of device could be small like a mobile phone depending upon how well the device is manufactured. 20 CHAPTER 2. ATT ENDANCE MANAGEMENT FRAMEWORK Figure 2. 3: Network Diagram 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY21 Figure 2. 4: ER Diagram 22 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK Figure 2. 5: Level 0 DFD Figure 2. 6: Level 1 DFD 2. 5. USING WIRELESS NETWORK INSTEAD OF LAN AND BRINGING PORTABILITY23 Figure 2. : Level 2 DFD 24 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK This device should have a wireless connection. Using this wireless connection, Figure 2. 8: Portable Device attendance taken would be updated automatically when device is in network of the nodes which are storing the attendance records. Database of enrolled ? ngerprints will be in this portable device. Size of enrolled database was 12. 1 MB when 150 ? ngerprints were enrolled in this project. So for 10000 students, atleast 807 MB or more space would be required for storing enrolled database. For this purpose, a removable memory chip could be used.We cannot use wireless LAN here because fetching data using wireless LAN will not be possible because of less range of wireless devices. So enrolled data would be on chip itself. Attendance results will be updated when portable device will be in the range of nodes which are storing attendance reports. We may update all the records online via the mobile network provided by di? erent companies. Today 3G network provides su? cient throughput which can be used for updating attendance records automatically without going near nodes. In such case, 2. 6. COMPARISON WITH OTHER STUDENT ATTENDANCE SYSTEMS 25 he need of database inside memory chip will not be mandatory. It will be fetched by using 3G mobile network from central database repository. The design of such a portable device is the task of embedded system engineers. 2. 5. 1 Using Portable Device In this section, we suggest the working of portable device and the method of using it for marking attendance. The device may either be having touchscreen input/display or buttons with lcd display . A software specially designed for the device will be running on it. Teachers will verify his/her ? ngerprint on the device before giving it to students for marking attendance.After verifying the teacher’s identity, software will ask for course and and other required information about the class which he or she is going to teach. Software will ask teacher the time after which device will not mark any attendance. This time can vary depending on the teacher’s mood but our suggested value is 25 minutes. This is done to prevent late entrance of students. This step will hardly take few seconds. Then students will be given device for their ? ngerprint identi? cation and attendance marking. In the continuation, teacher will start his/her lecture.Students will hand over the device to other students whose attendance is not marked. After 25 minutes or the time decided by teacher, device will not input any attendance. After the class is over, teacher will take device and will end the lecture. The main function of software running on the device will be ? ngerprint identi? cation of students followed by report generation and sending reports to servers using 3G network. Other functions will be downloading and updating the database available on the device from central database repository. 2. 6 Comparison with other student attendance systemsThere are various other kind of student attendance management systems available like RFID based student attendance system and GSM-GPRS based student attendance system. These systems have their own pros and cons. Our system is better because ? rst it saves time that could be used for teaching. Second is portability. Portability 26 CHAPTER 2. ATTENDANCE MANAGEMENT FRAMEWORK has its own advantage because the device could be taken to any class wherever it is scheduled. While GSM-GPRS based systems use position of class for attendance marking which is not dynamic and if schedule or location of the class changes, wrong attendance might be marked.Problem with RFID based systems is that students have to carry RFID cards and also the RFID detectors are needed to be installed. Nonetheless, students may give proxies easily using friend’s RFID card. These problems are not in our system. We used ? ngerprints as recognition criteria so proxies cannot be given. If portable devices are used, attendance marking will be done at any place and any time. So our student attendance system is far better to be implemented at NITR. Chapter 3 Fingerprint Identi? cation System An identi? cation system is one which helps in identifying an individual among any people when detailed information is not available. It may involve matching available features of candidate like ? ngerprints with those already enrolled in database. 3. 1 How Fingerprint Recognition works? Fingerprint images that are found or scanned are not of optimum quality. So we remove noises and enhance their quality. We extract features like minutiae and others for matching. If the sets of minutiae are matched with those in the database, we call it an identi? ed ? ngerprint. After matching, we perform post-matching steps which may include showing details of identi? ed candidate, marking attendance etc.A brief ? owchart is shown in next section. 3. 2 Fingerprint Identi? cation System Flowchart A brief methodology of our Fingerprint Identi? cation System is shown here in following ? owchart. Each of these are explained in the later chapters. 27 28 CHAPTER 3. FINGERPRINT IDENTIFICATION SYSTEM Figure 3. 1: Fingerprint Identi? cation System Flowchart Chapter 4 Fingerprint Enhancement The image acquired from scanner is sometimes not of perfect quality . It gets corrupted due to irregularities and non-uniformity in the impression taken and due to variations in the skin and the presence of the scars, humidity, irt etc. To overcome these problems , to reduce noise and enhance the de? nition of ridges against valleys, various techniques are applied as following. 4. 1 Segmentation Image segmentation [1] separates the foreground regions and the background regions in the image. The foreground regions refers to the clear ? ngerprint area which contains the ridges and valleys. This is the area of interest. The background regions refers to the regions which is outside the borders of the main ? ngerprint area, which does not contain any important or valid ? ngerprint information.The extraction of noisy and false minutiae can be done by applying minutiae extraction algorithm to the background regions of the image. Thus, segmentation is a process by which we can discard these background regions, which results in more reliable extraction of minutiae points. We are going to use a method based on variance thresholding . The background regions exhibit a very low grey – scale variance value , whereas the foreground regions have a very high variance . Firstly , the image is divided into blocks and the grey-scale variance is calculated for each block in the image .If the variance is less than the global threshold , then the block is assigned to be part of background region or else 29 30 CHAPTER 4. FINGERPRINT ENHANCEMENT it is part of foreground . The grey – level variance for a block of size S x S can be calculated as : 1 V ar(k) = 2 S S? 1 S? 1 (G(i, j) ? M (k))2 i=0 j=0 (4. 1) where Var(k) is the grey – level variance for the block k , G(i,j) is the grey – level value at pixel (i,j) , and M(k) denotes the mean grey – level value for the corresponding block k . 4. 2 Normalization Image normalization is the next step in ? ngerprint enhancement process.Normalization [1] is a process of standardizing the intensity values in an image so that these intensity values lies within a certain desired range. It can be done by adjusting the range of grey-level values in the image. Let G(i, j) denotes the grey-level value at pixel (i, j), and N(i, j) represent the normalized grey-level value at pi xel (i, j). Then the normalized image can de? ned as: ? ? M + 0 N (i, j) = ? M ? 0 V0 (G(i,j)? M )2 V V0 (G(i,j)? M )2 V , if I(i, j) > M , otherwise where M0 and V0 are the estimated mean and variance of I(i, j), respectively . 4. 3 Orientation estimation The orientation ? eld of a ? ngerprint image de? es the local orientation of the ridges contained in the ? ngerprint . The orientation estimation is a fundamental step in the enhancement process as the subsequent Gabor ? ltering stage relies on the local orientation in order to e? ectively enhance the ? ngerprint image. The least mean square estimation method used by Raymond Thai [1] is used to compute the orientation image. However, instead of estimating the orientation block-wise, we have chosen to extend their method into a pixel-wise scheme, which produces a ? ner and more accurate estimation of the orientation ? eld. The steps for calculating the orientation at pixel i, j) are as follows: 4. 3. ORIENTATION ESTIMATION 31 1. Fi rstly , a block of size W x W is centered at pixel (i, j) in the normalized ? ngerprint image. 2. For each pixel in the block, compute the gradients dx (i, j) and dy (i, j), which are the gradient magnitudes in the x and y directions, respectively. The horizontal Sobel operator[6] is used to compute dx(i, j) : [1 0 -1; 2 0 -2;1 0 -1] Figure 4. 1: Orientation Estimation 3. The local orientation at pixel (i; j) can then be estimated using the following equations: i+ W 2 j+ W 2 Vx (i, j) = u=i? W 2 i+ W 2 v=j? W 2 j+ W 2 2? x (u, v)? y (u, v) (4. 2) Vy (i, j) = u=i? W v=j? W 2 2 2 2 ? (u, v) ? ?y (u, v), (4. 3) ?(i, j) = 1 Vy (i, j) tan? 1 , 2 Vx (i, j) (4. 4) where ? (i, j) is the least square estimate of the local orientation at the block centered at pixel (i, j). 4. Smooth the orientation ? eld in a local neighborhood using a Gaussian ? lter. The orientation image is ? rstly converted into a continuous vector ? eld, which is de? ned as: ? x (i, j) = cos 2? (i, j), ? y (i, j) = sin 2 ? (i, j), (4. 5) (4. 6) where ? x and ? y are the x and y components of the vector ? eld, respectively. After 32 CHAPTER 4. FINGERPRINT ENHANCEMENT the vector ? eld has been computed, Gaussian smoothing is then performed as follows: w? w? 2 ?x (i, j) = w? u=? 2 w? v=? 2 G(u, v)? x (i ? uw, j ? vw), (4. 7) w? 2 w? 2 ?y (i, j) = w? u=? 2 w? v=? 2 G(u, v)? y (i ? uw, j ? vw), (4. 8) where G is a Gaussian low-pass ? lter of size w? x w? . 5. The ? nal smoothed orientation ? eld O at pixel (i, j) is de? ned as: O(i, j) = ? y (i, j) 1 tan? 1 2 ? x (i, j) (4. 9) 4. 4 Ridge Frequency Estimation Another important parameter,in addition to the orientation image, that can be used in the construction of the Gabor ? lter is the local ridge frequency. The local frequency of the ridges in a ? ngerprint is represented by the frequency image. The ? st step is to divide the image into blocks of size W x W. In the next step we project the greylevel values of each pixels located inside each block along a direction perpendicular to the local ridge orientation. This projection results in an almost sinusoidal-shape wave with the local minimum points denoting the ridges in the ? ngerprint. It involves smoothing the projected waveform using a Gaussian lowpass ? lter of size W x W which helps in reducing the e? ect of noise in the projection. The ridge spacing S(i, j) is then calculated by counting the median number of pixels between the consecutive minima points in the projected waveform.The ridge frequency F(i, j) for a block centered at pixel (i, j) is de? ned as: F (i, j) = 1 S(i, j) (4. 10) 4. 5. GABOR FILTER 33 4. 5 Gabor ? lter Gabor ? lters [1] are used because they have orientation-selective and frequencyselective properties. Gabor ? lters are called the mother of all other ? lters as other ? lter can be derived using this ? lter. Therefore, applying a properly tuned Gabor ? lter can preserve the ridge structures while reducing noise. An even-symmetric Gabor ? lter in the spati al domain is de? ned as : 1 x2 y2 G(x, y, ? , f ) = exp{? [ ? + ? ]} cos 2? f x? , 2 2 2 ? x ? y (4. 11) x? = x cos ? + y sin ? , (4. 12) y? ? x sin ? + y cos ? , (4. 13) where ? is the orientation of the Gabor ? lter, f is the frequency of the cosine wave, ? x and ? y are the standard deviations of the Gaussian envelope along the x and y axes, respectively, and x? and y? de? ne the x and y axes of the ? lter coordinate frame respectively. The Gabor Filter is applied to the ? ngerprint image by spatially convolving the image with the ? lter. The convolution of a pixel (i,j) in the image requires the corresponding orientation value O(i,j) and the ridge frequency value F(i,j) of that pixel . wy 2 wx 2 E(i, j) = u=? wx 2 w v=? 2y G(u, v, O(i, j), F (i, j))N (i ? u, j ? v), (4. 4) where O is the orientation image, F is the ridge frequency image, N is the normalized ? ngerprint image, and wx and wy are the width and height of the Gabor ? lter mask, respectively. 34 CHAPTER 4. FINGERPRINT ENHANCEMENT 4. 6 Binarisation Most minutiae extraction algorithms operate on basically binary images where there are only two levels of interest: the black pixels represent ridges, and the white pixels represent valleys. Binarisation [1] converts a greylevel image into a binary image. This helps in improving the contrast between the ridges and valleys in a ? ngerprint image, and consequently facilitates the extraction of minutiae.One very useful property of the Gabor ? lter is that it contains a DC component of zero, which indicates that the resulting ? ltered image has a zero mean pixel value. Hence, binarisation of the image can be done by using a global threshold of zero. Binarisation involves examining the grey-level value of every pixel in the enhanced image, and, if the grey-level value is greater than the prede? ned global threshold, then the pixel value is set to value one; else, it is set to zero. The outcome of binarisation is a binary image which contains two levels of i nformation, the background valleys and the foreground ridges. . 7 Thinning Thinning is a morphological operation which is used to remove selected foreground pixels from the binary images. A standard thinning algorithm from [1] is used, which performs this operation using two subiterations. The algorithm can be accessed by a software MATLAB via the ‘thin’ operation of the bwmorph function. Each subiteration starts by examining the neighborhood of every pixel in the binary image, and on the basis of a particular set of pixel-deletion criteria, it decides whether the pixel can be removed or not. These subiterations goes on until no more pixels can be removed.Figure 4. 2: (a)Original Image, (b)Enhanced Image, (c)Binarised Image, (d)Thinned Image Chapter 5 Feature Extraction After improving quality of the ? ngerprint image we extract features from binarised and thinned images. We extract reference point, minutiae and key(used for one to many matching). 5. 1 Finding the Refer ence Point Reference point is very important feature in advanced matching algorithms because it provides the location of origin for marking minutiae. We ? nd the reference point using the algorithm as in [2]. Then we ? nd the relative position of minutiae and estimate the orientation ? ld of the reference point or the singular point. The technique is to extract core and delta points using Poincare Index. The value of Poincare index is 180o , ? 180o and 0o for a core, a delta and an ordinary point respectively. Complex ? lters are used to produce blur at di? erent resolutions. Singular point (SP) or reference point is the point of maximum ? lter response of these ? lters applied on image. Complex ? lters , exp(im? ) , of order m (= 1 and -1) are used to produce ? lter response. Four level resolutions are used here:level 0, level 1, level 2, level 3.Level 3 is lowest resolution and level 0 is highest resolution. Only ? lters of ? rst order are used : h = (x + iy)m g(x, y) where g(x,y) is a gaussian de? ned as g(x, y) = exp? ((x2 + y 2 )/2? 2 ) and m = 1, ? 1. Filters are applied to the complex valued orientation tensor ? eld image z(x, y) = (fx + ify )2 and not directly to the image. Here f x is the derivative of the original image in the x-direction and f y is the derivative in the y-direction. To ? nd the position of a possible 35 36 CHAPTER 5. FEATURE EXTRACTION Figure 5. 1: Row 1: ? lter response c1k , k = 3, 2, and 1. Row 2: ? ter response c2k , k = 3, 2, and 1. SP in a ? ngerprint the maximum ? lter response is extracted in image c13 and in c23 (i. e. ?lter response at m = 1 and level 3 (c13 ) and at m = ? 1 and level 3 (c23 )). The search is done in a window computed in the previous higher level (low resolution). The ? lter response at lower level (high resolution) is used for ? nding response at higher level (low resolution). At a certain resolution (level k), if cnk (xj , yj ) is higher than a threshold an SP is found and its position (xj , yj ) and the complex ? lter response cnk (xj , yj ) are noted. 5. 2 5. 2. 1Minutiae Extraction and Post-Processing Minutiae Extraction The most commonly employed method of minutiae extraction is the Crossing Number (CN) concept [1] . This method involves the use of the skeleton image where the ridge ? ow pattern is eight-connected. The minutiae are extracted by scanning the local neighborhood of each ridge pixel in the image using a 3 x 3 window. The CN value is then computed, which is de? ned as half the sum of the di? erences between pairs of adjacent pixels in the eight-neighborhood. Using the properties of the CN as shown in ? gure 5, the ridge pixel can then be classi? d as a ridge ending, bifurcation or non-minutiae point. For example, a ridge pixel with a CN of one corresponds to a ridge ending, and a CN of three corresponds to a bifurcation. 5. 2. MINUTIAE EXTRACTION AND POST-PROCESSING Table 5. 1: Properties of Crossing Number CN Property 0 Isolated Point 1 Ridge Ending Point 2 Continu ing Ridge Point 3 Bifurcation Point 4 Crossing Point 37 Figure 5. 2: Examples of (a)ridge-ending (CN=1) and (b)bifurcation pixel (CN=3) 5. 2. 2 Post-Processing False minutiae may be introduced into the image due to factors such as noisy images, and image artefacts created by the thinning process.Hence, after the minutiae are extracted, it is necessary to employ a post-processing [1] stage in order to validate the minutiae. Figure 5. 3 illustrates some examples of false minutiae structures, which include the spur, hole, triangle and spike structures . It can be seen that the spur structure generates false ridge endings, where as both the hole and triangle structures generate false bifurcations. The spike structure creates a false bifurcation and a false ridge ending point. Figure 5. 3: Examples of typical false minutiae structures : (c)Triangle, (d)Spike (a)Spur, (b)Hole, 38 CHAPTER 5.FEATURE EXTRACTION 5. 2. 3 Removing Boundary Minutiae For removing boundary minutiae, we used pixel- density approach. Any point on the boundary will have less white pixel density in a window centered at it, as compared to inner minutiae. We calculated the limit, which indicated that pixel density less than that means it is a boundary minutiae. We calculated it according to following formula: limit = ( w w ? (ridgedensity)) ? Wf req 2 (5. 1) where w is the window size, Wf req is the window size used to compute ridge density. Figure 5. 4: Skeleton of window centered at boundary minutiaeFigure 5. 5: Matrix Representation of boundary minutiae Now, in thinned image, we sum all the pixels in the window of size w centered at the boundary minutiae. If sum is less than limit, the minutiae is considered as boundary minutiae and is discarded. 5. 3. EXTRACTION OF THE KEY 39 5. 3 5. 3. 1 Extraction of the key What is key? Key is used as a hashing tool in this project. Key is small set of few minutiae closest to reference point. We match minutiae sets, if the keys of sample and query ? ngerprin ts matches. Keys are stored along with minutiae sets in the database.Advantage of using key is that, we do not perform full matching every time for non-matching minutiae sets, as it would be time consuming. For large databases, if we go on matching full minutiae set for every enrolled ? ngerprint, it would waste time unnecessarily. Two types of keys are proposed – simple and complex. Simple key has been used in this project. Figure 5. 6: Key Representation Simple Key This type of key has been used in this project. Minutiae which constitute this key are ten minutiae closest to the reference point or centroid of all minutiae, in sorted 40 CHAPTER 5. FEATURE EXTRACTION order. Five ? lds are stored for each key value i. e. (x, y, ? , t, r). (x, y) is the location of minutiae, ? is the value of orientation of ridge related to minutia with respect to orientation of reference point, t is type of minutiae, and r is distance of minutiae from origin. Due to inaccuracy and imperfection of reference point detection algorithm, we used centroid of all minutiae for construction of key. Complex Key The complex key stores more information and is structurally more complex. It stores vector of minutiae in which next minutiae is closest to previous minutiae, starting with reference point or centroid of all minutiae.It stores < x, y, ? , t, r, d, ? >. Here x,y,t,r,? are same, d is distance from previous minutiae entry and ? is di? erence in ridge orientation from previous minutiae. Data: minutiaelist = Minutiae Set, refx = x-cordinate of centroid, refy = y-cordinate of centroid Result: Key d(10)=null; for j = 1 to 10 do for i = 1 to rows(minutiaelist) do d(i) Chapter 6 Partitioning of Database Before we partition the database, we perform gender estimation and classi? cation. 6. 1 Gender Estimation In [3], study on 100 males and 100 females revealed that signi? cant sex di? erences occur in the ? ngerprint ridge density.Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Henceforth, gender of the candidate can be estimated on the basis of given ? ngerprint data. Based on this estimation, searching for a record in the database can be made faster. Method for ? nding mean ridge density and estimated gender: The highest and lowest values for male and female ridge densities will be searched. If ridge density of query ? ngerprint is less than the lowest ridge density value of females, the query ? ngerprint is obviously of a male. Similarly, if it is higher than highest ridge density value of males, the query ? gerprint is of a female. So the searching will be carried out in male or female domains. If the value is between these values, we search on the basis of whether the mean of these values is less than the density of query image or higher. 41 42 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 1: Gender Estimation 6. 1. GENDER ESTIMATION Data: Size of Database = N; Ridge Density of query ? ngerprint = s Result: Estima ted Gender i. e. male or female maleupperlimit=0; femalelowerlimit=20; mean=0; for image < femalelowerlimit then femalelowerlimit 43 if s < maleupperlimit then estimatedgender 44 CHAPTER 6.PARTITIONING OF DATABASE 6. 2 Classi? cation of Fingerprint We divide ? ngerprint into ? ve classes – arch or tented arch, left loop, right loop, whorl and unclassi? ed. The algorithm for classi? cation [4] is used in this project. They used a ridge classi? cation algorithm that involves three categories of ridge structures:nonrecurring ridges, type I recurring ridges and type II recurring ridges. N1 and N2 represent number of type I recurring ridges and type II recurring ridges respectively. Nc and Nd are number of core and delta in the ? ngerprint. To ? nd core and delta, separate 135o blocks from orientation image. 35o blocks are shown in following ? gures. Figure 6. 2: 135o blocks of a ? ngerprint Based on number of such blocks and their relative positions, the core and delta are found using Poincare index method. After these, classi? cation is done as following: 1. If (N2 > 0) and (Nc = 2) and (Nd = 2), then a whorl is identi? ed. 2. If (N1 = 0) and (N2 = 0) and (Nc = 0) and (Nd = 0), then an arch is identi? ed. 3. If (N1 > 0) and (N2 = 0) and (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm[4]. 4. If (N2 > T2) and (Nc > 0), then a whorl is identi? ed. 5.If (N1 > T1) and (N2 = 0) and (Nc = 1) then classify the input using the core and delta assessment algorithm[4]. 6. If (Nc = 2), then a whorl is identi? ed. 7. If (Nc = 1) and (Nd = 1), then classify the input using the core and delta assessment algorithm[4]. 8. If (N1 > 0) and (Nc = 1), then classify the input using the core and delta assessment algorithm. 6. 3. PARTITIONING 9. If (Nc = 0) and (Nd = 0), then an arch is identi? ed. 10. If none of the above conditions is satis? ed, then reject the ? ngerprint. 45 Figure 6. 3: Fingerprint Classes (a)Left Loop, (b)Right Lo op, (c)Whorl, (d1)Arch, (d2)Tented Arch . 3 Partitioning After we estimate gender and ? nd the class of ? ngerprint, we know which ? ngerprints to be searched in the database. We roughly divide database into one-tenth using the above parameters. This would roughly reduce identi? cation time to one-tenth. 46 CHAPTER 6. PARTITIONING OF DATABASE Figure 6. 4: Partitioning Database Chapter 7 Matching Matching means ? nding most appropriate similar ? ngerprint to query ? ngerprint. Fingerprints are matched by matching set of minutiae extracted. Minutiae sets never match completely, so we compute match score of matching. If match score satis? s accuracy needs, we call it successful matching. We used a new key based one to many matching intended for large databases. 7. 1 Alignment Before we go for matching, minutiae set need to be aligned(registered) with each other. For alignment problems, we used hough transform based registration technique similar to one used by Ratha et al[5]. Minutiae alignment is done in two steps minutiae registration and pairing. Minutiae registration involves aligning minutiae using parameters < ? x, ? y, ? > which range within speci? ed limits. (? x, ? y) are translational parameters and ? is rotational parameter.Using these parameters, minutiae sets are rotated and translated within parameters limits. Then we ? nd pairing scores of each transformation and transformation giving maximum score is registered as alignment transformation. Using this transformation < ? x, ? y, ? >, we align query minutiae set with the database minutiae set. Algorithm is same as in [5] but we have excluded factor ? s i. e. the scaling parameter because it does not a? ect much the alignment process. ? lies from -20 degrees to 20 degrees in steps of 1 or 2 generalized as < ? 1 , ? 2 , ? 3 †¦? k > where k is number of rotations applied.For every query minutiae i we check if ? k + ? i = ? j where ? i and ? j are orientation 47 48 CHAPTER 7. MATCHING parameters of ith minutia of query minutiae set and j th minutia of database minutiae set. If condition is satis? ed, A(i,j,k) is ? agged as 1 else 0. For all these ? agged values, (? x, ? y) is calculated using following formula: ? (? x , ? y ) = qj ? ? cos? sin? ? ? ? pi , (7. 1) ?sin? cos? where qj and pi are the coordinates of j th minutiae of database minutiae set and ith minutiae of query minutiae set respectively. Using these < ? x, ?y, ? k > values, whole query minutiae set is aligned.This aligned minutiae set is used to compute pairing score. Two minutiae are said to be paired only when they lie in same bounding box and have same orientation. Pairing score is (number of paired minutiae)/(total number of minutiae). The i,j,k values which have highest pairing score are ? nally used to align minutiae set. Co-ordinates of aligned minutiae are found using the formula: ? qj = ? cos? sin? ? ? ? pi + (? x , ? y ), (7. 2) ?sin? cos? After alignment, minutiae are stored in sorted order of their di stance from their centroid or core. 7. 2 Existing Matching TechniquesMost popular matching technique of today is the simple minded n2 matching where n is number of minutiae. In this matching each minutiae of query ? ngerprint is matched with n minutiae of sample ? ngerprint giving total number of n2 comparisons. This matching is very orthodox and gives headache when identi? cation is done on large databases. 7. 3 One to Many matching Few algorithms are proposed by many researchers around the world which are better than normal n2 matching. But all of them are one to one veri? cation or one to one identi? cation matching types. We developed a one to many matching technique which uses key as the hashing tool.Initially, we do not match minutiae sets instead we per- 7. 3. ONE TO MANY MATCHING 49 form key matching with many keys of database. Those database ? ngerprints whose keys match with key of query ? ngerprint, are allowed for full minutiae matching. Key matching and full matching ar e performed using k*n matching algorithm discussed in later section. Following section gives method for one to many matching. Data: Query Fingerprint; Result: Matching Results; Acquire Fingerprint, Perform Enhancement, Find Fingerprint Class, Extract Minutiae, Remove Spurious and Boundary Minutiae, Extract Key,Estimate Gender; M . 3. 1 Method of One to Many Matching The matching algorithm will be involving matching the key of the query ? ngerprint with the many(M) keys of the database. Those which matches ,their full matching will be processed, else the query key will be matched with next M keys and so on. 50 Data: Gender, Class, i; Result: Matching Results; egender CHAPTER 7. MATCHING if keymatchstatus = success then eminutiae 7. 4 Performing key match and full matching Both key matching and full matching are performed using our k*n matching technique. Here k is a constant(recommended value is 15) chosen by us.In this method, we match ith minutiae of query set with k unmatched minu tiae of sample set. Both the query sets and sample sets must be in sorted order of distance from reference point or centroid. ith minutia of query minutiae list is matched with top k unmatched minutiae of database minutiae set. This type of matching reduces matching time of n2 to k*n. If minutiae are 80 in number and we chose k to be 15, the total number of comparisons will reduce from 80*80=6400 to 80*15=1200. And this means our matching will be k/n times faster than n2 matching. 7. 5. TIME COMPLEXITY OF THIS MATCHING TECHNIQUE 51 Figure 7. : One to Many Matching 7. 5 Time Complexity of this matching technique Let s = size of the key, n = number of minutiae, N = number of ? ngerprints matched till successful identi? cation, k = constant (see previous section). There would be N-1 unsuccessful key matches, one successful key match, one successful full match. Time for N-1 unsuccessful key matches is (N-1)*s*k (in worst case), for successful full match is s*k and for full match is n*k. Total time is (N-1)*s*k+n*k+s*k = k(s*N+n). Here s=10 and we have reduced database to be searched to 1/10th ,so N matching technique, it would have been O(Nn2 ).For large databases, our matching technique is best to use. Averaging for every ? ngerprint, we have O(1+n/N) in this identi? cation process which comes to O(1) when N >> n. So we can say that our identi? cation system has constant average matching time when database size is millions. Chapter 8 Experimental Analysis 8. 1 Implementation Environment We tested our algorithm on several databases like FVC2004, FVC2000 and Veri? nger databases. We used a computer with 2GB RAM and 1. 83 GHz Intel Core2Duo processor and softwares like Matlab10 and MSAccess10. 8. 2 8. 2. 1 Fingerprint Enhancement Segmentation and NormalizationSegmentation was performed and it generated a mask matrix which has values as 1 for ridges and 0 for background . Normalization was done with mean = 0 and variance = 1 (? g 8. 1). Figure 8. 1: Normalized Image 52 8. 2. FINGERPRINT ENHANCEMENT 53 8. 2. 2 Orientation Estimation In orientation estimation, we used block size = 3*3. Orientations are shown in ? gure 8. 2. Figure 8. 2: Orientation Image 8. 2. 3 Ridge Frequency Estimation Ridge density and mean ridge density were calculated. Darker blocks indicated low ridge density and vice-versa. Ridge frequencies are shown in ? gure 8. 3. Figure 8. 3: Ridge Frequency Image 8. 2. 4Gabor Filters Gabor ? lters were employed to enhance quality of image. Orientation estimation and ridge frequency images are requirements for implementing gabor ? lters. ?x and ? y are taken 0. 5 in Raymond Thai, but we used ? x = 0. 7 and ? y = 0. 7. Based on these values , we got results which were satis? able and are shown in ? gure 8. 4. 54 CHAPTER 8. EXPERIMENTAL ANALYSIS Figure 8. 4: Left-Original Image, Right-Enhanced Image 8. 2. 5 Binarisation and Thinning After the ? ngerprint image is enhanced, it is then converted to binary form, and submitted to the thinni ng algorithm which reduces the ridge thickness to one pixel wide.Results of binarisation are shown in ? gure 8. 5 and of thinning are shown in ? gure 8. 6. Figure 8. 5: Binarised Image 8. 3. FEATURE EXTRACTION 55 Figure 8. 6: Thinned Image 8. 3 8. 3. 1 Feature Extraction Minutiae Extraction and Post Processing Minutiae Extraction Using the crossing number method, we extracted minutiae. For this we used skeleton image or the thinned image. Due to low quality of ? ngerprint, a lot of false and boundary minutiae were found. So we moved forward for post-processing step. Results are shown in ? gure 8. 7 and 8. 8. Figure 8. 7: All Extracted Minutiae 56 CHAPTER 8. EXPERIMENTAL ANALYSISFigure 8. 8: Composite Image with spurious and boundary minutiae After Removing Spurious and Boundary Minutiae False minutiae were removed using method described in earlier section. For removing boundary minutiae, we employed our algorithm which worked ? ne and minutiae extraction results are shown in table 8 . 2. Results are shown in ? gure 8. 9 and 8. 10. Figure 8. 9: Minutiae Image after post-processing As we can see from table 8. 2 that removing boundary minutiae considerably reduced the number of false minutiae from minutiae extraction results. 8. 4. GENDER ESTIMATION AND CLASSIFICATION 57 Figure 8. 0: Composite Image after post-processing Table 8. 1: Average Number of Minutiae before and after post-processing DB After After Removing After Removing Used Extraction Spurious Ones Boundary Minutiae FVC2004DB4 218 186 93 FVC2004DB3 222 196 55 8. 3. 2 Reference Point Detection For reference point extraction we used complex ? lters as described earlier. For a database size of 300, reference point was found with success rate of 67. 66 percent. 8. 4 8. 4. 1 Gender Estimation and Classi? cation Gender Estimation Average ridge density was calculated along with minimum and maximum ridge densities shown in table 8. . Mean ridge density was used to divide the database into two parts. This reduce d database size to be searched by half. Based on the information available about the gender of enrolled student, we can apply our gender estimation algorithm which will further increase the speed of identi? cation. 8. 4. 2 Classi? cation Fingerprint classi? cation was performed on both original and enhanced images. Results were more accurate on the enhanced image. We used same algorithm as in sec 6. 2 to classify the ? ngerprint into ? ve classes – arch, left loop, right loop, whorl and 58 CHAPTER 8.EXPERIMENTAL ANALYSIS Figure 8. 11: Plotted Minutiae with Reference Point(Black Spot) Table 8. 2: Ridge Density Calculation Results Window Minimum Maximum Mean Total Average Size Ridge Ridge Ridge Time Time Taken Density Density Density Taken Taken 36 6. 25 9. 50 7. 87 193. 76 sec 1. 46 sec unclassi? ed. This classi? cation was used to divide the database into ? ve parts which would reduce the database to be searched to one-? fth and ultimately making this identi? cation process ? ve times faster. Results of classi? cation are shown in table 8. 4, 8. 5 and 8. 6. 8. 5 EnrollingAt the time of enrolling personal details like name, semester, gender, age, roll number etc. were asked to input by the user and following features of ? ngerprint were saved in the database (1)Minutiae Set (2)Key (3)Ridge Density (4)Class Total and average time taken for enrolling ? ngerprints in database is shown in table 8. 6. MATCHING Table 8. 3: Classi? cation Results on Original Image Class No. of (1-5) Images 1 2 2 2 3 3 4 4 5 121 Table 8. 4: Classi? cation Results on Enhanced Image Class No. of (1-5) Images 1 8 2 3 3 3 4 6 5 112 59 8. 7. All the personal details were stored in the MS Access database and were modi? d by running sql queries inside matlab. Fingerprint features were stored in txt format inside a separate folder. When txt ? le were used, the process of enrolling was faster as compared to storing the values in MS Access DB. It was due to the overhead of connections, ru nning sql queries for MS Access DB. 8. 6 Matching Fingerprint matching is required by both veri? cation and identi? cation processes. 8. 6. 1 Fingerprint Veri? cation Results Fingerprint veri? cation is the process of matching two ? ngerprints against each other to verify whether they belong to same person or not. When a ? gerprint matches with the ? ngerprint of same individual, we call it true accept or if it doesn’t, we call it false reject. In the same way if the ? ngerprint of di? erent individuals match, we call it a false accept or if it rejects them, it is true reject. False Accept Rate (FAR) and False Reject Rate (FRR) are the error rates which are used to express matching trustability. FAR is de? ned by the formula : 60 CHAPTER 8. EXPERIMENTAL ANALYSIS Table 8. 5: Time taken for Classi? cation Image Average Total Taken Time(sec) Time(sec) Original 0. 5233 69. 07 Enhanced 0. 8891 117. 36 Table 8. : Time taken for Enrolling No. of Storage Average Total Images Type Tim e(sec) Time(hrs) 294 MS Access DB 24. 55 2. 046 60 MS Access DB 29. 37 0. 49 150 TXT ? les 15. 06 1. 255 F AR = FA ? 100, N (8. 1) FA = Number of False Accepts, N = Total number of veri? cations FRR is de? ned by the formula : FR ? 100, N F RR = (8. 2) FR = Number of False Rejects. FAR and FRR calculated over six templates of Veri? nger DB are shown in table 8. 8. This process took approximately 7 hours. 8. 6. 2 Identi? cation Results and Comparison with Other Matching techniques Fingerprint identi? cation is the process of identifying a query ? gerprint from a set of enrolled ? ngerprints. Identi? cation is usually a slower process because we have to search over a large database. Currently we match minutiae set of query ? ngerprint with the minutiae sets of enrolled ? ngerprints. In this project, we store key in the database at the time of enrolling. This key as explained in sec 5. 3 helps in 8. 6. MATCHING Table 8. 7: Error Rates FAR FRR 4. 56 12. 5 14. 72 4. 02 61 Figure 8. 12: G raph: Time taken for Identi? cation vs Size of Database(key based one to many identi? cation) reducing matching time over non-matching ? ngerprints. For non-matching enrolled ? gerprints, we don’t perform full matching, instead a key matching. Among one or many keys which matched in one iteration of one to many matching, we allow full minutiae set matching. Then if any full matching succeeds, we perform post matching steps. This identi? cation scheme has lesser time complexity as compared to conventional n2 one to one identi? cation. Identi? cation results are shown in table 8. 9. The graph of time versus N is shown in ? gure 8. 13. Here N is the index of ? ngerprint to be identi? ed from a set of enrolled ? ngerprints. Size of database of enrolled ? ngerprints was 150. So N can vary from