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

THE EVOLUTION AND ADVANCEMENT OF A GRADUATE COURSE IN BIOMETRICS

Stephen J. Elliott 1 and Eric P. Kukula 2

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, [email protected] 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, [email protected]

Abstract During the Fall of 2002 a biometrics course was developed to encourage cross-disciplinary education and research, which addressed two core areas: biometric technologies and their applications. The goal of the course initially was to provide students with a functional knowledge in biometrics that they could transfer to a career in the information security and technology industry. However, since the initial offering in 2002, the course has been modified to accommodate students with diverse backgrounds and interests. This paper discusses the evolution and advancements the course has undertaken since the initial offering and the framework for future modifications to increase the skill sets of the intended audience. Index Terms curriculum development, biometrics, graduate education

INTRODUCTION

Biometrics is defined as the automated recognition of individuals based on their behavioral and biological characteristics [1]. Traditionally biometrics has been limited to academic disciplines such as Computer Science, Electrical Engineering, and Statistics. For example, algorithm development typically occurred within computer science, while speech and computer vision developed in electrical engineering. As biometric technology evolves and matures, additional disciplines have gained an interest in biometrics including; Technology, Ergonomics, Management, and Political Science. The realization of converging disciplines in biometric technology was accepted by the authors and resulted in the creation of a multi-disciplinary class in Biometric Technology and Applications in the Fall semester of 2002, with the aim at encouraging cross-disciplinary education and research. The course benefited from the integration of research and engagement through the deployment of biometrics equipment into an educational environment. However, as the technology has advanced, the curricula, specifically the mathematical prerequisites, of the students taking the course have not. Therefore a dichotomy exists where enrolled students are not prepared mathematically or statistically for the projects that the newer technology would allow them to pursue. Their ability to develop an interest and fully

engage in research projects in the laboratory is also hampered by their mathematical backgrounds. At the same time however, we must not forget the core mission of the College of Technology which directs faculty to balance the competing demands of research and education.

PREVIOUS COURSE OFFERINGS

The original course development and syllabus for Biometric Technology and Applications is outlined in detail in [2]. The course was taught from the viewpoint of systems integrator, purchaser and evaluator. In addition, the course examined the advantages and disadvantages of the individual biometric technologies, the fundamentals of testing and evaluation, writing technical reports and presentations, and understanding the process of biometric standards. The first course was offered in the Fall of 2002. Twenty students participated in the course, with a majority of students being junior or senior undergraduate students in Computer Information Systems Technology or Industrial Technology. The course was introductory in nature, covering the general aspects of biometric testing and evaluation. At the same time, the lab was fairly small with limited equipment which necessitated the overview style of the course.

The second semester the course was offered saw an increase in the number of non-undergraduate Technology majors. Twenty seven students took part in the class, with seven from Aviation Technology, Computer Science, and Information Security. Furthermore the course was added as a School of Management elective. To accommodate the interdisciplinary audience discussions in management, algorithm development, and integration were added to the course. Furthermore, the lab moved to larger facilities that included 11 workstations and enabled the course to have a more substantial laboratory experience and also enabled students to work on more complicated research projects.

During the 2003-2004 academic year the instructors of the course developed a laboratory manual so that students could complete a more independent style of research while interacting with the biometrics technology. Enrollment remained at about 20-25 students per semester. The course started to incorporate more applied research than previously – typically testing and evaluation

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of commercially available products, thus giving students contact with companies in the biometrics industry. However the needs required by the research indicated that the course would have to become more statistically orientated. During the 2004-05 school year a course textbook was developed specifically for the purpose of this class as there was no appropriate text available for the biometrics practitioner. In addition to the text, the class moved into e-learning, as all readings, assignments, and directions were maintained in WebCT Vista™. Semester projects were more varied, ranging from investigating new hand geometry techniques, to securing a manufacturing environment with biometric technologies, and netorking biometric devices. The lab continued to grow, and moved again into its current location, as shown in Figure 1. Over $700,000 worth of equipment had been purchased or donated resulting in students having access to many different biometric modalities. The number of students remained constant from previous semesters but as the class continued to move towards data collection and analysis, it was clear that the course needed to be adapted to provide more information on statistics. In addition to the lectures, students used the equipment purchased and donated to the Biometrics Standards, Performance, and Assurance (BSPA) Laboratory in the Department of Industrial Technology.

FIGURE. 1

BSPA LABORATORY IN THE DEPARTMENT OF INDUSTRIAL TECHNOLOGY.

REASONS FOR COURSE CHANGES

The course has benefited from the integration of research and engagement through the deployment of the equipment into an educational environment. However as was mentioned earlier, a dichotomy has developed between the preparation of the students via their prerequisites, and the knowledge they need to more

successfully interact with the laboratory and research projects. The course was designed as an introductory course in biometric technology and applications. As such, it has had the mission of teaching College of Technology students an overview of the individual biometric modalities and usually consists of a semester project that provides students with the knowledge to implement biometric technologies into their workplace [1]. With the increase in statistical analysis, a balance had to be struck to cater to the students in the course through a challenging course structure, yet at the same time maintain interest so that they can understand the material, and gain a benefit for the course. This was done through case studies and practical experiences. Discussions with the students highlighted a “fear” of statistics, mainly because the only statistics courses they had participated in were either back in high school, or early on in their collegiate career. Further examination of typical students’ plans of study revealed a deficiency in higher mathematics courses at the collegiate level. For example, the Industrial Technology curriculum includes a freshman (100) level algebra and trigonometry course, and a junior (300) level course in statistical quality. The plan of study in Computer and Information Technology has students taking two 200 level Mathematics courses which deal with calculus. There was 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 level in the two major areas of Industrial Technology and Computer Information Technology would not have had any previous experience with statistical software such as SAS™, Minitab™, and SPSS™. Furthermore, they would have no prior experience with MATLAB™ either. Identifying the missing gaps of knowledge is one thing, but students in the College of Technology tend to learn best if they can interact with data in a hands-on environment. The easiest solution was to have the students create data themselves (keystroke dynamics was chosen due the very small feature set), and analyze the data from there – introducing statistical and mathematical concepts through experimentation as opposed to lectures.


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So the course was adapted from its previous version as described in [1], to include mathematical and statistical concepts. The first exercise was to initially collect data so that students could examine the repeatability of samples, and undertake some elementary statistical calculations. With this assignment they learn about concepts such as outliers, the Gaussian distribution, kurtosis, skewness, and the basics of data collection and data integrity. From these basic steps, probability emerges and must be understood by students, as biometrics do not return binary scores. This leads into a discussion on hypotheses development – whether an individual is going to be accepted into the system, or whether there are any statistically significant differences in image quality – two examples that the instructors use to convey probabilistic and mathematical concepts to the students. It is envisaged that the next run of the course will include some pre- and post testing of the students knowledge now that the first semester run through and development has been completed. The course continues with a discussion on power and significance, and this leads nicely into the development of a threshold value, False Match Rates, and False Non-Match Rates. Students can relate this information back to the initial keystroke data collection. Another adjustment to the course has been to introduce more applied research activities. The lab often undertakes testing and evaluation for commercial entities and this provides opportunities for students to interact with real world problems and data. This semester, there are three major projects – the first two projects are continuations of course projects from previous semesters and involve Hand Geometry in the Recreation Center [21], and the implementation of Biometrics in a Manufacturing Environment [22]. The third research project examines how hand readers perform at an elderly residential home. In this project, students in the class have to go out to the residential home and collect hand data. They will then analyze the scores, and provide statistical evidence on how the hand reader performs with an elderly population vis-à-vis an 18-25 population. All of these projects provide the students with valuable learning opportunities that require them to collect data, provide feedback on the data collection, statistically examine the data, and write up a technical report. This change to the course has resulted in the first seven weeks being devoted to mathematical and statistical properties. The next part of the course examine the individual biometric modalities. These too were discussed in [1], although there have been some additions to the course. Given that the students have had seven weeks of mathematics and statistics, they can now analyze data using software tools that was previously explained to them in a class lecture. It is hoped that this will increase their understanding of the various topics.

CURRENT COURSE OFFERING

The Fall 2005 course was updated and redesigned to provide students with the ability to make “biometrics happen” in their place of work. Topics for the course included: • Discussing biometrics and their broader role in

Automatic Identification and Data Capture (AIDC) technologies.

• Detailed exposure and lab activities on each biometric modality.

• Design experiments, enabling students to design testing and evaluation protocols that can be used during the course or in the graduate research.

• Introduction of mathematical and statistical concepts, outlining for the technologist basic elements of concepts used in biometrics.

• Introduction to the standards development process and biometric standards initiatives.

• Privacy Issues • Vulnerabilities and attacks to biometric systems. • Implementation project which gives students practical

experience designing, building, implementing a biometric system in an operational environment.

The design of this particular offering was a balance between practical and theoretical. To balance the theoretical out, industry representatives were brought in to discuss individual biometric modalities, as well as their applications and real-world implementations. Table 1 below outlines the similarities and differences between the initial offering in 2002 and the current offering in 2005.

TABLE I COMPARISON OF COURSE SYLLABI FOR COURSES TAUGHT IN 2002 AND

2005

Week 2002 2005

1 Introduction to Biometrics Introduction to Biometrics

Human Subjects Biometrics and the Role in AIDC

2 Biometric Technology Overview

Dynamic Signature Verification

Biometrics and Aviation (Case Study) Definitions

3 Taxonomy and Testing Procedures

Mathematical Concepts and Statistics

4

Legislation, Standards, Testing and Regulatory Bodies

Mathematical Concepts and Statistics

5 Electronic Signatures Mathematical Concepts and Statistics

Forgery Experiments

6 Electronic Signature Analysis Human Subjects Testing

7 Hand Geometry Fingerprint Recognition 8 Fingerprint Recognition Fingerprint Recognition

Biometric Security Issues Fingerprint Image Quality


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9 Face Recognition Iris Recognition

Face Recognition at Purdue University Airport (Case Study) Keystroke Analysis

10 Iris Recognition Creating and Maintaining Databases

11 Voice Recognition

Human Factors and Biometric Device Performance

Face Recognition (2D and 3D)

12 Biometric Implementations in Law Enforcement Voice Recognition

Biometric Standards 13 Future of Biometrics Site Survey (Airport) 14 Review of the Course Group Presentations

FURTHER WORK

The enhanced course will have run for one semester (Fall 2005) to see what improvements to the adapted syllabus need to be made. The Spring semester will see a series of pre- and post tests that will evaluate the progress of these changes. In addition to formative evaluation methods, a summative evaluation will also be designed to measure the overall effectiveness of the program. This evaluation will focus on student learning and their application of the course principles into their career, including strengths and deficiencies in their skill sets, as well as a survey to identify the careers chosen by the graduates and quantify the number of students pursuing graduate education [23-27].

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