Becoming a good designer of biometric systems requires mastering the application of the theory on biometric traits.

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 assigned by MS Teams. The project must be done in 48 hours and is as difficult as the assigned homework during lectures. If you are also interested in submitting questions and interact with tutors and me, you can ask for the registration at this link.

Download the course's slides.

Bibliography

1. A. Jain et al., Handbook of Biometrics, Springer.
2. B. Bhanu and A. Kumar, Deep learning in biometrics, Springer.
3. K. Saeed, New direction in behavioural biometrics, CRC Press.
4. V. Murino et al., Group and crowd behavior for computer vision, Academic Press.
5. D. Maltoni et al., Handbook of fingerprint recognition, Springer.
6. H. Liu, Face Detection and Recognition on Mobile Devices, Elsevier.
7. M. Vatsa et al., Deep learning in biometrics, CRC Press.