Course content

• Fingerprint recognition
• Vein recognition
• Face recognition including three dimensional data
• Iris recognition
• Multimodal biometrics
• Attack mechanisms against biometric system (components)
• Privacy Enhancing Technologies including homomorphic encryption
• Sample quality assessment technologies

Students will learn the capabilities of GPU-consuming Deep Learning approaches to solve biometric tasks. They will also be motivated to investigate, whether low-resource consuming hand-crafted algorithms can solve a task equally well in a sustainable spirit.

Learning outcome

Knowledge:

• The candidate possesses knowledge at the most advanced frontier in the field of biometrics.
• The candidate has mastered academic theory and scientific methods in biometrics.
• The candidate is capable of considering suitability and use of different methods and processes in research in the field of biometrics.
• The candidate is capable of contributing to development of new knowledge, theories, methods, interpretations and forms of documentation in biometrics.

Skills:

• The candidate is capable of formulating problems, planning and completing research projects in biometrics.
• The candidate is capable of doing research and development at a high international level. The candidate is capable of handling complex academic tasks.
• The candidate can challenge established knowledge and practice in biometrics.

More specifically after the course, the candidate should have the following capabilities:

• Developed a systematic understanding of biometric systems and their capabilities.
• Mastered multiple modality-specific feature extraction and have the ability to evaluate their suitability for given acquisition characteristics
• Developed in-depth insights into statistical methods and tools for biometrics and their performance evaluation
• The ability to synthesize multi-modal analysis methods and solve score normalisation problems in fusion systems
• The ability to appraise and differentiate threats to biometric reference data, judging and realizing adequate protection mechanisms accordingly
• The ability to perform in-depth assessment of biometric component placement within a security system demonstrated
• The ability to design and defend a biometric security system when provided with a threat scenario

General competence:

• The candidate is capable of identifying relevant - and possibly new - ethical problems and exercising research in biometrics with academic integrity.
• The candidate is capable of managing complex interdisciplinary tasks and projects.
• The candidate is capable of disseminating the results of research and development in biometrics through approved national and international publication channels.
• The candidate is capable of taking part in debates in international forums within the field of biometrics.
• The candidate is capable of considering the need for, taking initiative to and engaging in innovation in the field of biometrics.

More specifically the candidate:

• will have the competence to demonstrate the ability to design a biometric system suitable for a given scenario
• judge the relevance of ethical and privacy issues investigate for a given scenario
• technical solutions and evaluate them in a critical analysis.
• synthesize new ideas during evaluation phase
• communicate with peers in the biometric community in terms of reviewing research topics
• manage team work

Learning methods and activities

• Lectures
• Assignments
• Seminar(s)

Additional information: Seminar with term paper presentation

Compulsory requirements: None

About the course in general: In case of doubt the English version is the main description

Specific conditions

Course materials

[1] A. Jain, P. Flynn, A. Russ: Handbook of Biometrics, Springer, 2008

[2] S. Li and A. Jain: Handbook of Face Recognition, Springer, 2011

[3] D. Maltoni , D. Maio, A. Jain, S Prabhakar: Handbook of Fingerprint Recognition, Springer, 2009

[4] S. Marcel, M. Nixon, J. Fierrez, N. Evans: Handbook of Biometric Anti-Spoofing - Presentation Attack Detection, Springer, 2019

[5] P. Tuyls, B. Skroic, T. Kevenaar: Security with Noisy Data, Springer, 2007

[6] A. Uhl, C. Busch, S. Marcel, R. Veldhuis: Handbook of Vascular Biometrics, Springer, 2020

[7] C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, C. Busch: Handbook of Digital Face Manipulation and Detection, Springer, 2021