(2006) The evolution and advancement of a graduate course in biometrics


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(2006) The evolution and advancement of a graduate course in biometrics

  1. 1. THE EVOLUTION AND ADVANCEMENT OF A GRADUATE COURSE IN BIOMETRICS Stephen J. Elliott 1 and Eric P. Kukula 2 Abstract  During the Fall of 2002 a biometrics course engage in research projects in the laboratory is also was developed to encourage cross-disciplinary education hampered by their mathematical backgrounds. At the and research, which addressed two core areas: biometric same time however, we must not forget the core mission technologies and their applications. The goal of the of the College of Technology which directs faculty to course initially was to provide students with a functional balance the competing demands of research and knowledge in biometrics that they could transfer to a education. career in the information security and technology industry. However, since the initial offering in 2002, the PREVIOUS COURSE OFFERINGS course has been modified to accommodate students with The original course development and syllabus diverse backgrounds and interests. This paper discusses for Biometric Technology and Applications is outlined in the evolution and advancements the course has detail in [2]. The course was taught from the viewpoint of undertaken since the initial offering and the framework systems integrator, purchaser and evaluator. In addition, for future modifications to increase the skill sets of the the course examined the advantages and disadvantages of intended audience. the individual biometric technologies, the fundamentals of testing and evaluation, writing technical reports and Index Terms  curriculum development, biometrics, graduate education presentations, and understanding the process of biometric standards. The first course was offered in the Fall of 2002. INTRODUCTION Twenty students participated in the course, with a majority of students being junior or senior undergraduate Biometrics is defined as the automated students in Computer Information Systems Technology or recognition of individuals based on their behavioral and Industrial Technology. The course was introductory in biological characteristics [1]. Traditionally biometrics has nature, covering the general aspects of biometric testing been limited to academic disciplines such as Computer and evaluation. At the same time, the lab was fairly small Science, Electrical Engineering, and Statistics. For with limited equipment which necessitated the overview example, algorithm development typically occurred style of the course. within computer science, while speech and computer The second semester the course was offered saw vision developed in electrical engineering. As biometric an increase in the number of non-undergraduate technology evolves and matures, additional disciplines Technology majors. Twenty seven students took part in have gained an interest in biometrics including; the class, with seven from Aviation Technology, Technology, Ergonomics, Management, and Political Computer Science, and Information Security. Science. The realization of converging disciplines in Furthermore the course was added as a School of biometric technology was accepted by the authors and Management elective. To accommodate the resulted in the creation of a multi-disciplinary class in interdisciplinary audience discussions in management, Biometric Technology and Applications in the Fall algorithm development, and integration were added to the semester of 2002, with the aim at encouraging cross- course. Furthermore, the lab moved to larger facilities that disciplinary education and research. The course benefited included 11 workstations and enabled the course to have a from the integration of research and engagement through more substantial laboratory experience and also enabled the deployment of biometrics equipment into an students to work on more complicated research projects. educational environment. However, as the technology has During the 2003-2004 academic year the advanced, the curricula, specifically the mathematical instructors of the course developed a laboratory manual so prerequisites, of the students taking the course have not. that students could complete a more independent style of Therefore a dichotomy exists where enrolled students are research while interacting with the biometrics technology. not prepared mathematically or statistically for the Enrollment remained at about 20-25 students per projects that the newer technology would allow them to semester. The course started to incorporate more applied pursue. Their ability to develop an interest and fully research than previously – typically testing and evaluation 1 Stephen Elliott, Ph.D., Assistant Professor, Biometrics Standards Performance, and Assurance Laboratory, Department of Industrial Technology, Purdue University, 401 North Grant Street, West Lafayette, IN 47906, USA, elliott@purdue.edu 2 Eric Kukula, Research Assistant, Biometrics Standards Performance, and Assurance Laboratory, Department of Industrial Technology, Purdue University, 401 North Grant Street, West Lafayette, IN 47906, USA, kukula@purdue.edu ©2006 WCCSETE March 19 - 22, 2006, São Paulo, BRAZIL World Congress on Computer Science, Engineering and Technology Education 89
  2. 2. of commercially available products, thus giving students successfully interact with the laboratory and research contact with companies in the biometrics industry. projects. However the needs required by the research indicated that The course was designed as an introductory the course would have to become more statistically course in biometric technology and applications. As such, orientated. it has had the mission of teaching College of Technology During the 2004-05 school year a course students an overview of the individual biometric textbook was developed specifically for the purpose of modalities and usually consists of a semester project that this class as there was no appropriate text available for the provides students with the knowledge to implement biometrics practitioner. In addition to the text, the class biometric technologies into their workplace [1]. With the moved into e-learning, as all readings, assignments, and increase in statistical analysis, a balance had to be struck directions were maintained in WebCT Vista™. Semester to cater to the students in the course through a challenging projects were more varied, ranging from investigating course structure, yet at the same time maintain interest so new hand geometry techniques, to securing a that they can understand the material, and gain a benefit manufacturing environment with biometric technologies, for the course. This was done through case studies and and netorking biometric devices. The lab continued to practical experiences. Discussions with the students grow, and moved again into its current location, as shown highlighted a “fear” of statistics, mainly because the only in Figure 1. Over $700,000 worth of equipment had been statistics courses they had participated in were either back purchased or donated resulting in students having access in high school, or early on in their collegiate career. to many different biometric modalities. The number of Further examination of typical students’ plans of study students remained constant from previous semesters but revealed a deficiency in higher mathematics courses at the as the class continued to move towards data collection collegiate level. For example, the Industrial Technology and analysis, it was clear that the course needed to be curriculum includes a freshman (100) level algebra and adapted to provide more information on statistics. In trigonometry course, and a junior (300) level course in addition to the lectures, students used the equipment statistical quality. The plan of study in Computer and purchased and donated to the Biometrics Standards, Information Technology has students taking two 200 level Performance, and Assurance (BSPA) Laboratory in the Mathematics courses which deal with calculus. There was Department of Industrial Technology. one statistics course in the Computer Information Technology plan of study. Although useful, none of these courses relate to the mathematics and statistics covered in the biometrics field. So the challenge therefore is to present the technology in an easy to understand format, and also teach some of the most important mathematical concepts. ADAPTATION OF OTHER COURSES A review of various biometric literature was undertaken – books that were basically introductory in nature [2-20], to examine what types of statistics and mathematics were being used, and whether any of the major topics were being excluded from the previous editions of the course because of their mathematical nature. It must be noted that students at the undergraduate FIGURE. 1 level in the two major areas of Industrial Technology and BSPA LABORATORY IN THE DEPARTMENT OF INDUSTRIAL TECHNOLOGY. Computer Information Technology would not have had any previous experience with statistical software such as SAS™, Minitab™, and SPSS™. Furthermore, they would REASONS FOR COURSE CHANGES have no prior experience with MATLAB™ either. Identifying the missing gaps of knowledge is one thing, The course has benefited from the integration of but students in the College of Technology tend to learn research and engagement through the deployment of the best if they can interact with data in a hands-on equipment into an educational environment. However as environment. The easiest solution was to have the was mentioned earlier, a dichotomy has developed students create data themselves (keystroke dynamics was between the preparation of the students via their chosen due the very small feature set), and analyze the prerequisites, and the knowledge they need to more data from there – introducing statistical and mathematical concepts through experimentation as opposed to lectures. ©2006 WCCSETE March 19 - 22, 2006, São Paulo, BRAZIL World Congress on Computer Science, Engineering and Technology Education 90
  3. 3. So the course was adapted from its previous CURRENT COURSE OFFERING version as described in [1], to include mathematical and statistical concepts. The first exercise was to initially The Fall 2005 course was updated and collect data so that students could examine the redesigned to provide students with the ability to make repeatability of samples, and undertake some elementary “biometrics happen” in their place of work. Topics for the statistical calculations. With this assignment they learn course included: about concepts such as outliers, the Gaussian distribution, • Discussing biometrics and their broader role in kurtosis, skewness, and the basics of data collection and Automatic Identification and Data Capture (AIDC) data integrity. From these basic steps, probability emerges technologies. and must be understood by students, as biometrics do not • Detailed exposure and lab activities on each biometric return binary scores. This leads into a discussion on modality. hypotheses development – whether an individual is going • Design experiments, enabling students to design to be accepted into the system, or whether there are any testing and evaluation protocols that can be used statistically significant differences in image quality – two during the course or in the graduate research. examples that the instructors use to convey probabilistic • Introduction of mathematical and statistical concepts, and mathematical concepts to the students. It is envisaged outlining for the technologist basic elements of that the next run of the course will include some pre- and concepts used in biometrics. post testing of the students knowledge now that the first • Introduction to the standards development process and semester run through and development has been biometric standards initiatives. completed. The course continues with a discussion on • Privacy Issues power and significance, and this leads nicely into the • Vulnerabilities and attacks to biometric systems. development of a threshold value, False Match Rates, and • Implementation project which gives students practical False Non-Match Rates. Students can relate this experience designing, building, implementing a information back to the initial keystroke data collection. biometric system in an operational environment. Another adjustment to the course has been to The design of this particular offering was a introduce more applied research activities. The lab often balance between practical and theoretical. To balance the undertakes testing and evaluation for commercial entities theoretical out, industry representatives were brought in to and this provides opportunities for students to interact discuss individual biometric modalities, as well as their with real world problems and data. This semester, there applications and real-world implementations. Table 1 are three major projects – the first two projects are below outlines the similarities and differences between continuations of course projects from previous semesters the initial offering in 2002 and the current offering in and involve Hand Geometry in the Recreation Center 2005. [21], and the implementation of Biometrics in a Manufacturing Environment [22]. The third research TABLE I project examines how hand readers perform at an elderly COMPARISON OF COURSE SYLLABI FOR COURSES TAUGHT IN 2002 AND residential home. In this project, students in the class have 2005 to go out to the residential home and collect hand data. Week 2002 2005 They will then analyze the scores, and provide statistical Introduction to evidence on how the hand reader performs with an elderly 1 Introduction to Biometrics Biometrics population vis-à-vis an 18-25 population. All of these Biometrics and the Role Human Subjects in AIDC projects provide the students with valuable learning Biometric Technology Dynamic Signature opportunities that require them to collect data, provide 2 Overview Verification feedback on the data collection, statistically examine the Biometrics and Aviation data, and write up a technical report. (Case Study) Definitions This change to the course has resulted in the first Taxonomy and Testing Mathematical Concepts 3 Procedures and Statistics seven weeks being devoted to mathematical and statistical Legislation, Standards, properties. The next part of the course examine the Testing and Regulatory Mathematical Concepts individual biometric modalities. These too were discussed 4 Bodies and Statistics in [1], although there have been some additions to the Mathematical Concepts course. Given that the students have had seven weeks of 5 Electronic Signatures and Statistics Forgery Experiments mathematics and statistics, they can now analyze data Electronic Signature using software tools that was previously explained to 6 Analysis Human Subjects Testing them in a class lecture. It is hoped that this will increase 7 Hand Geometry Fingerprint Recognition their understanding of the various topics. 8 Fingerprint Recognition Fingerprint Recognition Fingerprint Image Biometric Security Issues Quality ©2006 WCCSETE March 19 - 22, 2006, São Paulo, BRAZIL World Congress on Computer Science, Engineering and Technology Education 91
  4. 4. 9 Face Recognition Iris Recognition 8. Elliott, S., Biometric Technology: A primer for Aviation Face Recognition at Purdue Technology Students. International Journal Of Applied Aviation University Airport (Case Studies, 2002. 3(2): p. 311-322. Study) Keystroke Analysis 9. Elliott, S., Differentiation of signature traits vis-a-vis mobile and Creating and table-based digitizers. ETRI Journal, 2004. 26(6): p. 641-646. 10 Iris Recognition Maintaining Databases 10. Fairhurst, M.C., Signature verification revisited: promoting Human Factors and practical exploitation of biometric technology. Electronics & Biometric Device Communication Engineering Journal, 1997: p. 273-280. 11 Voice Recognition Performance 11. Howell, A., Introduction to Face Recognition, in Intelligent Face Recognition (2D Biometric Techniques in Fingerprint and Face Recognition, L. and 3D) Jain, et al., Editors. 1999, CRC Press: Boca Raton, FL. p. 219-238. Biometric Implementations 12. Jain, A., R. Bolle, and S. Pankanti, Introduction to Biometrics, in 12 Voice Recognition Biometrics: Personal Identification in Networked Society, A. Jain, in Law Enforcement R. Bolle, and S. Pankanti, Editors. 1999, Klewer Academic Biometric Standards Publishers Group: Norwell, MA. 13 Future of Biometrics Site Survey (Airport) 13. Jain, A., L. Hong, and S. Pankanti, Biometrics: Promising frontiers 14 Review of the Course Group Presentations for emerging identification market. 2000: Comm ACM. p. 91-98. 14. Moore, G. and D. vonMinden, The History of Fingerprints, FURTHER WORK onin.com, Editor. 2003. 15. Newton, H. and J. Woodward, Biometrics: A Technical Primer. The enhanced course will have run for one 2001, RAND: Santa Monica, CA. 16. Pankanti, S., R.M. Bolle, and A. Jain, Biometrics: The Future of semester (Fall 2005) to see what improvements to the Identification. Computer, 2000. 33(2): p. 46-49. adapted syllabus need to be made. The Spring semester 17. Rizvi, S., P. Phillips, and H. Moon, The FERET Verification will see a series of pre- and post tests that will evaluate Testing Protocol for Face Recogntion Algorithms. 1998, U.S. the progress of these changes. In addition to formative Army Research Laboratory. p. 74. 18. Sickler, N., An Evaluation of Fingerprint Quality across an evaluation methods, a summative evaluation will also be Elderly Population vis-à-vis 18-25 Year Olds, in Industrial designed to measure the overall effectiveness of the Technology. 2003, Purdue University: West Lafayette, IN. program. This evaluation will focus on student learning 19. Wayman, J., Fundamentals of Biometric Authentication and their application of the course principles into their Techniques, in National Biometric Test Center Collected Works, J. Wayman, Editor. 2000, National Biometric Test Center.: San Jose, career, including strengths and deficiencies in their skill CA. p. 1-20. sets, as well as a survey to identify the careers chosen by 20. Wayman, J., A Definition of Biometrics, in National Biometric Test the graduates and quantify the number of students Center Collected Works, J. Wayman, Editor. 2000, National pursuing graduate education [23-27]. Biometric Test Center.: San Jose, CA. p. 21-24. 21. Kukula, E. P., & Elliott, S. J. (2005, October). Implementation of hand geometry at Purdue University’s Recreational Center. An REFERENCES analysis of user perspectives and system performance. Proceedings of the 39th Annual International Carnahan Conference on Security 1. Kukula, E., N. Sickler, and S. Elliott. Adaptation and Technology (ICCST) (pp. 83-88). Las Palmas de G. C., Spain implementation to a graduate course development in biometrics. in 22. Modi, S. K., & Elliott, S. J. (2005, October). Securing the World Conference on Engineering and Technology Education. Manufacturing Environment using Biometrics. Proceedings of the 2004. Santos, Brazil: ASEE. 39th Annual International Carnahan Conference on Security 2. 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