BIOMETRIC SYSTEMS

AIM

Biometric systems, that rely on physiological and/or behavioral characteristics (e.g., fingerprint, face, iris, voice ...), for personal authentication, are becoming ubiquitous: from national e-ID cards, to accessing secure sites (e.g. airports), from web-based applications to law enforcement checks (e.g. AFIS), these systems that go beyond the usage of traditional username/password/card combinations are securing our lives & creating added value every day. In this course, design, implementation, and evaluation of unimodal & multimodal biometric systems with primers on relevant signal processing & pattern recognition topics will be covered. The intersection with cryptography and future prospects will also be highlighted.

CONTENT

This course contains; Introduction to biometric systems, general characteristics, building blocks, applications ,Identity verification methods: biometrics based and others ,Relevant pattern recognition and signal processing topics, feature extractors & classifiers ,Fingerprint recognition: sensors, attributes, performance, classification, indexing, uniqueness.,Fingerprint recognition, features, performance, classification, indexing, and uniqueness. ,Face recognition ,Iris recognition,Voice recognition,Gait, vein, palmprint, signature recognition & novel modalities ,Multimodal biometric systems ,Cryptography & biometrics: system security & template privacy ,Standard databases, evaluation & tests ,Future prospects, research directions, challenges; project evaluations ,Future prospects, research directions, challenges; project evaluations .

1. 1

   Designs a biometric authentication system that meets the given conditions.

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

2. 2

   Evaluates alternative biometric systems in terms of performance, cost and feasibility.

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

3. 3

   It supports software developers to implement a successful biometric system in institutions.

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

4. 4

   Makes informed decisions by taking into account the limits and advantages of biometric systems over traditional identification systems.

   Taught by: Lecture Method · Assessed by: Traditional Written Exam, Homework, Project Task

WEEKLY PLAN

1. WEEK 1

   Introduction to biometric systems, general characteristics, building blocks, applications

   Preparation: Ref.1 Ch. 1

2. WEEK 2

   Identity verification methods: biometrics based and others

   Preparation: Ref. 1 Ch. 1

3. WEEK 3

   Relevant pattern recognition and signal processing topics, feature extractors & classifiers

   Preparation: Ref. 4 Ch. 1

4. WEEK 4

   Fingerprint recognition: sensors, attributes, performance, classification, indexing, uniqueness.

   Preparation: Ref. 2 Ch. 2-4, 5, 8

5. WEEK 5

   Fingerprint recognition, features, performance, classification, indexing, and uniqueness.

   Preparation: Ref. 2 Ch. 2-4, 5, 8

6. WEEK 6

   Face recognition

   Preparation: Ref.1 Ch. 3

7. WEEK 7

   Iris recognition

   Preparation: Ref.1 Ch. 4

8. WEEK 8

   Voice recognition

   Preparation: Ref.1 Ch. 8

9. WEEK 9

   Gait, vein, palmprint, signature recognition & novel modalities

   Preparation: Ref.1 Ch. 6&9&10

10. WEEK 10

    Multimodal biometric systems

    Preparation: Ref.3 Ch. 2&3

11. WEEK 11

    Cryptography & biometrics: system security & template privacy

    Preparation: Ref.1 Ch. 19

12. WEEK 12

    Standard databases, evaluation & tests

    Preparation: Ref.1 Ch. 24&25

13. WEEK 13

    Future prospects, research directions, challenges; project evaluations

    Preparation: Publication websites

14. WEEK 14

    Future prospects, research directions, challenges; project evaluations

    Preparation: Publication websites

ASSESSMENT

• Rate of Midterm Exam to Success50%
• Rate of Final Exam to Success50%

WORKLOAD

READING

• A.K. Jain, P. Flynn, A.A. Ross, Handbook of Biometrics, Springer, 2008.
• 1- D. Maltoni, D. Maio, A.K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, 2. Ed., Springer, 2009. 2- A. Ross, K. Nandakumar, and A.K. Jain, Handbook of Multibiometrics, 2006. 3- R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, 2. Ed., Wiley, 2001.

TEACHING STAFF

• Prof.Dr. Bahadır Kürşat GÜNTÜRKCOORDINATOR