Coursebooks 2017-2018

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Biometrics

UNIL-402

Lecturer(s) :

Drygajlo Andrzej

Language:

English

Summary

This course is an introduction to analysis, modeling and interpretation of biometric data for biometric person recognition, forensic biometrics, cybersecurity and behavioral biometrics in man-machine communication.

Content

Fundamentals of Biometrics

Identity and Biometrics, Individuality of Biometric Dat, Recognition, Verification, Identification and Authentication.

Analysis, Modeling and Interpretation of Biometric Data

Mathematical Tools for Biometric Signal Processing, Pattern Recognition and Machine Learning, Sensing and Storage, Representation and Feature Extraction, Models of Features for Recognition and Classification, Enrollment and Template Creation, Biometric System Errors, Evaluation of Biometric Systems.

Leading Biometric Technology

Biological Characteristics (fingerprints, face (2D and 3D), hand palms, veins and geometry, eye irises and retinas), Behavioral Characteristics (dynamic signature, voice, gait, keystroke dynamics), Biological Traces (DNA, odour), Technologies under development, Synthetic Biometric Data Generation

Multimodal Biometrics

Biometric Standards

Biometric Systems

Small, Medium and Large Scale, Biometric Systems, Integration of biometrics with other existing technologies (identity documents, smart cards, smart phones and smart pads, databases, e-technologies, transmission of biometric data), Behavior Biometrics in Human-Machine Communication

Securing Biometrics Data/Systems and Biometric Encryption

Biometric Applications

Security (Physical and Logical Access), Law Enforcement and Forensic Applications, Cybersecurity, Government and Military Sector, Financial Sector, Healthcare, Travel and Immigration

Privacy and Legal Issues

Keywords

biometrics, identity verification, biometric recognition, fingerprint, face, iris, palmprint, hand veins, signature, voice, gait, DNA

Learning Prerequisites

Recommended courses

Statistical signal and data processing through applications, Pattern classification and machine learning

Important concepts to start the course

Digital Signal Processing, Pattern Recognition

Learning Outcomes

By the end of the course, the student must be able to:

Transversal skills

Teaching methods

Ex cathedra with exercises and demonstrations in classroom

Expected student activities

attendance at lectures, completing exercises and applying demonstrations at home

Assessment methods

Oral exam

Supervision

Office hours No
Assistants No
Forum No

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

A.K. Jain, A. Ross, K. Nandakumar, "Introduction to Biometrics", Springer-Verlag, New York, 2011.

Notes/Handbook

A.K. jain, P. Flynn, A. Ross, "Handbook of Biometrics", Springer-Verlag, New York, 2008.

D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar, 'Handbook of Fingerprint Recognition', Springer-Verlag, New York, 2003, (second edition 2009).

M.J. Burge, K.W. Bowyer, (Eds), 'Handbook of Iris Recognition', Springer Verlag, London, 2013.

A. Jain, S. Li, (Eds) ' Handbook of Face Recognition', Springer Verlag, London, 2011.

A. Ross, K. Nandakumar, A. Jain, 'Handbook of Multibiometrics', Springer, New York, 2006.

Websites
Moodle Link

Prerequisite for

Semester Project, Master Thesis, Ph.D. Thesis

In the programs

  • Communication Systems - master program, 2017-2018, Master semester 1
    • Semester
      Fall
    • Exam form
      Oral
    • Credits
      3
    • Subject examined
      Biometrics
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
  • Communication Systems - master program, 2017-2018, Master semester 3
    • Semester
      Fall
    • Exam form
      Oral
    • Credits
      3
    • Subject examined
      Biometrics
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks
  • Information security minor, 2017-2018, Autumn semester
    • Semester
      Fall
    • Exam form
      Oral
    • Credits
      3
    • Subject examined
      Biometrics
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      1 Hour(s) per week x 14 weeks

Reference week

MoTuWeThFr
8-9
9-10
10-11
11-12
12-13
13-14
14-15 GCA1416
15-16
16-17 GCA1416
17-18
18-19
19-20
20-21
21-22
Lecture
Exercise, TP
Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German