This instructor is available by email at firstname.lastname@example.org, by telephone (962-4261), and during office hours (CI 2042). Office hours are posted on the instructor’s home page and located on the door to his office. In addition, students can arrange to meet with the professor outside of normal office hours by contacting him via email or phone or schedule using Outlook.
You are expected to take an active role in your learning in this course. This includes:
1. regular attendance,
2. actively participating in class, discussions,
3. reading the handouts, articles, book chapters, etc., and
4. completing all course requirements.
You are encouraged to study with your classmates outside of class, as published research studies have demonstrated the efficacy this learning paradigm. However, assignments that are not designated as a “group” assignment must be performed independently. Severe punishment for openly plagiarizing/cheating will handed out.
Prerequisites: Statistics 215, Discrete Structures and a high-level programming course, i.e. Java, C-, C++, Python, etc. Consideration can and will be made for special circumstances, e.g. students who may not have the prerequisites.
T. Dunstone and N. Yager, Biometric System and Data Analysis: Design, Evaluation, and Data Mining, Springer 2010. ISBN 978-1-4419-4595-2.
A.K. Jain, P. Flynn and A. A. Ross, Handbook of Biometrics, Springer, 2010, ISBN: 978-1441943750
• N. K. Ratha and V. Govindaraju, Advances in Biometrics: Sensors, Algorithms and Systems, Springer, 2008
• D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, Springer Verlag, 2003.
• A. K. Jain, R. Bolle, S. Pankanti (Eds.), BIOMETRICS: Personal Identification in Networked Society, Kluwer Academic Publishers, 1999.
• J. Wayman, A.K. Jain, D. Maltoni, and D. Maio (Eds.), Biometric Systems: Technology, Design and Performance Evaluation, Springer, 2004.
• In addition, a number of journals, including Pattern Recognition, Pattern Recognition Letters, IEEE Trans. Pattern Analysis & Machine Intelligence (PAMI), IEEE Trans. Image Processing, IEEE Trans. Speech and Audio Processing and IEEE Security & Privacy Magazine routinely publish papers on biometrics.
Introduction: Automatic and reliable identification of individuals for issuing official documents (e.g., passport and visa) and providing access to secure facilities (e.g., military base) and proprietary information (e.g., corporate websites) has become an essential part of our modern networked society. Biometric recognition systems utilize the physiological or behavioral characteristics of an individual for identification. By using biometrics, it is possible to establish an identity based on "who you are", rather than by "what you possess" (e.g., an ID card) or "what you remember" (e.g., a password). The events of 9/11 have generated huge interest in the design, deployment and evaluation of biometric systems. In this course we will study the design of various biometric systems based on fingerprints, voice, face, hand geometry, palmprint, iris, retina, and other modalities. Multimodal biometric systems that use two or more of the above characteristics will be discussed. Biometric system performance and issues related to the security and privacy aspects of these systems will also be addressed.
Participation: Regular class attendance is required. Student will not be allowed to makeup assignments, exams, or projects.
Numeric Score Letter Grade Quality Points
90.0 - 100 A 4.00
80.0 - 89.5 B 3.00
70.0 - 79.5 C 2.00
60.0 - 69.5 D 1.00
00.0 - 59.5 F 0.00
The students will work in teams of 2/3 on a particular deployment of biometric systems. Biometrics is a rapidly growing area of research and commercialization with estimated market shares of billions. This case study is intended to introduce the students to the biometric market place and its variety of uses. Many uses of biometric technology are still being developed, e.g. digital photo album organization, automated vending of age-controlled products (e.g. cigarettes), authentication for mobile devices, securing web-based financial transactions, intention determination, etc.
The students will create a 6-page paper (12-pointt font, single space, 1-inch margins, with 5-10 references) and a 15-minute presentation (15 – 20 slides) for the selected case study. The case study will be due by in the first third of the term. Some suggested applications are:
• Biometrics in Transportation (Airports, Railroads, etc)
• Biometrics in Commercial Applications (Grocery Stores, Hotels, etc)
• Biometrics in Medical Services (Healthcare, Document Security, etc)
• Biometrics in Forensics (Criminal Justice, Disaster Victim Identification)
• Biometrics and Smart Cards (Federal Identify Cards, National ID, etc)
http://www.smartcardalliance.org/pdf/industry_info/smartcardhandbook.pdf (United States)
http://www.identitycards.gov.uk (united kingdom)
• Biometrics in Department of Defense
• Standardization of Biometric Systems
• Soft-Biometrics in Marketing, Retail, or Web.
The students will work in teams of 2/3 on a class project. Possible topics for the project are:
• Soft-biometrics: determining individual characteristics from traditional biometric signals (data)
• Implementation and evaluation of a biometric modality (must be relevant to current research)
• Image quality assessment
• Template characteristics with regard to the change over time
• Performance comparisons between human and computer systems
• Performance comparisons among different biometric sensors and modalities
Students will submit a project report at the end of the semester and will present their project results to the class. The class will evaluate each teams project and provide a peer score for the each team. The peer score will be combined with the instructor’s evaluation of the project to achieve the final grade for the project.
The University Learning Center (ULC) provides free programs and services that support students as they develop independent learning strategies, personal responsibility, intellectual maturity, transferable skills, and a respect for diverse learning experiences. It is important to remember that tutoring is not remediation: The ULC serves all students who want to increase the quality of their learning. The following programs offer different levels of support, each staffed by faculty-recommended and trained peer tutors.
Writing Services provides one-on-one and small group writing consultations for all students for any academic writing purpose. Face-to-Face and Online tutoring is available.
Math Services helps students improve their math skills by providing tutoring for all Math and Statistics courses or any course with a math or statistics component. No appointment is needed during open lab hours.
Learning Services provides content tutoring for all Basic Studies courses. Learning Services also provides Study Skills support for students seeking to strengthen their general academic skills. All Learning Services tutoring is by appointment only.
The University Learning Center is located on the first floor of Westside Hall (WE 1056). Phone: 962-7857; Website: www.uncw.edu/ulc;
Students are responsible for submitting their own work. Students who cooperate on oral or written examinations or work without authorization share the responsibility for violation of academic principles, and the students are subject to disciplinary action even when one of the students is not enrolled in the course where the violation occurred.
If you have a disability and need reasonable accommodation in this course, you should inform the instructor of this fact in writing within the first week of class or as soon as possible. If you have not already done so, you must register with the Office of Disability Services in Westside Hall (extension 3746) and obtain a copy of your Accommodation Letter. You should then meet with your instructor to make mutually agreeable arrangements based on the recommendations of the Accommodation Letter.