Image based biometry

The course relies mostly on computer vision, as most biometrics technologies are based on it. Students interested in cutting edge technology, much of which is still in a research stage, are the intended target for the course. The main content (will evolve due to developments in the field):

1. Biometry basics

2. Biometrical modalities

3. Structure of a typical biometric system

4. Recognition/verification/identification

5. Metrics

6. Conditions for correct comparisons of the systems (databases, frameworks)

7. Performance and usefulness of the systems

8. Computer vision as the foundation of the biometric systems

   —–

9. Fingerprint

   1. Acquisition

   2. Quality assessment and quality improvement

   3. Processing

   4. Singular points, minutiae, ridges

   5. Matching

      —–

10. Iris

    1. Acquisition

    2. Quality improvement

    3. Processing (segmentation, normalization, coding)

    4. Feature points

    5. Matching

       —–

11. Face

    1. Acquisition

    2. Sub-modalities

    3. Processing

    4. Feature points (appearance/
       model/texture-based approach)

    5. Matching

       —–

12. Gait

    1. Acquisition

    2. Influence of dynamics

    3. Processing (appearance/
       model-based approach)

    4. Dynamic feature points

    5. Matching

       —–

13. Ear

    1. Acquisition

    2. Processing

    3. Feature points

    4. Matching

       —–

14. Multi-biometric systems / multi-modality / fusions

15. Key problems of modalities/systems (research challenges)

     

The lectures introduce the approaches and explain their operation. At tutorial the knowledge is applied to practical problems in Matlab and open source tools.

Objectives of the course:

• Student gains good overview over the biometry and with it related computer vision methods that set foundations of biometric systems.

• Student gets acquainted with the flow of the research work.

• Student gets good foundation for doctoral study.

  Gained student competences:

• Knows the terminology and principles of identity analysis.
• Knows the scope of the biometric technologies and their (dis)advantages.
• Knows how the system works from the acquisition to decision.
• Understands the processing flow for each biometric modality.
• Knows some limitations of biometric systems.
• Is able to critically consider older and newer modalities and how they can work together.
• Is familiar with some open problems/challenges in biometry.

Lectures and tutorial, individual work on assignments/project, presentations of outcomes.