---
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 course gives the overview of the research field. 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.