Computer vision is defined as 

Computer vision deals with many problems, including image formation, image processing, model fitting and optimization, image alignment and stitching, motion estimation, 3d models, and computational photography. On the other hand, biometric technologies are among the main applications of computer vision, although they are not strictly based on visual signals.

Biometric recognition is a particular pattern recognition problem, which also includes techniques of machine learning and artificial intelligence. For this reason, I introduce in the theoretical part the fundamentals implemented during the laboratory exercises.

These show several use-cases and scenarios that real contexts and environments must deal with. During lectures, you will be encouraged to improve the understanding of each topic by doing some homework. The implementation of each homework has an assigned score; this score is taken under consideration when the final exam will be given.

The final exam consists of

• a project that is assigned at least seven days before the day set for the exam;
• an oral interview with the teacher, aimed at explaining the main issues in the project, how they have been solved, and possible alternatives to the proposed solutions - preparation of slides will be allowed but the ability to explain specific part of the source code, as well as high level concepts, will be required to pass the exam.

The subscription to the teams platform is recommended at this link.

More information about this course.

NOTE: BIOMETRIC TECHNOLOGIES AND BEHAVIORAL SECURITY course is deactivated. Slides are available by enrolling at this link.

Bibliography

1. R. Szelisky, Computer Vision: Algorithms and Applications 2nd Edition, Springer Cham, https://doi.org/10.1007/978-3-030-34372-9.
2. A. Jain et al., Handbook of Biometrics, Springer.
3. B. Bhanu and A. Kumar, Deep learning in biometrics, Springer.
4. K. Saeed, New direction in behavioural biometrics, CRC Press.
5. V. Murino et al., Group and crowd behavior for computer vision, Academic Press.
6. D. Maltoni et al., Handbook of fingerprint recognition, Springer.
7. H. Liu, Face Detection and Recognition on Mobile Devices, Elsevier.
8. M. Vatsa et al., Deep learning in biometrics, CRC Press.