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
a. Acquisition
b. Quality assessment and quality improvement
c. Processing
d. Singular points, minutiae, ridges
e. Matching
10. Iris
a. Acquisition
b. Quality improvement
c. Processing (segmentation, normalization, coding)
d. Feature points
e. Matching
11. Face
a. Acquisition
b. Sub-modalities
c. Processing
d. Feature points (appearance/
model/texture-based approach)
e. Matching
12. Gait
a. Acquisition
b. Influence of dynamics
c. Processing (appearance/
model-based approach)
d. Dynamic feature points
e. Matching
13. Ear
a. Acquisition
b. Processing
c. Feature points
d. 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.
Student work includes three assignments and a final exam. The deadlines for the three assignments are approximately around beginning to mid of November, beginning to mid of December and beginning to mid of January.
- nosilec: Peter Peer