Course contents at a glance

The term biometrics is derived from the Greek words bios (life) and métron (measure).

Biometrics refers to the study and use of methods to detect and measure the characteristics of living organisms and draw comparative classifications and laws.

Biometrics finds applications in biology, medicine, genetics, in the agricultural and forestry sciences, environmental science and other related fields.

The modern meaning of the term biometrics used in Computer Science, and consequently of the term biometric system, explicitly mainly refers to the automatic identification or verification of the identity of a person based on physical or behavioral characteristics.

The course provides basic knowledge and skills necessary for the design and development of automated systems for the recognition of people on the based biometric features.

Important note: Proficiency in programming is required to be able to carry out the final project

The course at a glance

Introduction to biometric systems

Performance evaluation

Reliability of recognition results

Face detection

Face recognition 2D and 3D

Ear recognition

Iris recognition

Basics on fingerprints recognition

Other biometrics

Multibiometric systems

Reference stuff

A.K. Jain, P. Flynn, A.A. Ross, Handbook of Biometrics, Springer, 2008.

H. Wechsler, Reliable Face Recognition Methods: System Design, Implementation and Evaluation, Springer, 2007.

A.Ross, K. Nandakumar; A.K. Jain. Handbook of Multibiometrics. Springer, 2006

Course slides

(Notice that each "lesson" below spans more classes)

Lesson 0 - Course Presentation

Lesson 1 - Introduction to Biometric Systems

Lesson 2 - Performance Evaluation

Lesson 2bis - More on performance evaluation - version updated on 13 of October

Lesson 3 - Response reliability

Lesson 4 - Introduction to face biometrics and to face localization

Lesson 5 - Face localization: two example approaches

Lesson 6 - Face recognition in 2D

Lesson 7 - Face recognition in 3D

Lesson 8 - Face recognition: evaluation

Lesson 9 - Ear recognition

Lesson 10 - Iris recognition:

Lesson 11 - Fingerprint recognition

Lesson 12 - Multibiometric systems

Lesson 13 - Example Solutions (what my group works on)