Hochschule Darmstadt - Fb Informatik

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Module:Biometric Systems
Module numbers:41.5094 [PVL 41.5095; Module 41.50940]
Study programme:Dualer Master 2021 - Katalog AS: Anwendungs- und systemorientierte Module
Dualer Master 2021 - Vertiefung IS: IT Sicherheit
Master 2021 - Katalog AS: Anwendungs- und systemorientierte Module
Master 2021 - Vertiefung IS: IT Sicherheit
Dualer Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
Dualer Master 2013 - Vertiefung IS: IT-Sicherheit
Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
Master 2013 - Vertiefung IS: IT-Sicherheit
MN Data Science 2016 - Katalog DS-I: Data Science - Informatik
Type of course:V+S = Lecture+Seminar
Weekly hours:2+2
Credit Points:6
Exam:written exam (90 min, with tasks from the master task catalog)
Registering for examexplicitly and independent of booking
PVL (e.g. Practical):graded (The PVL is achieved with the term paper, which will be graded based on the submitted paper and the oral presentation of the findings. The presentation will take place in the seminar.)
PVL percentage:50%
Frequency of offering:each summer semester (lastly in SS 2022)
Required knowledge:IT-Security
Learning objectives:After the course, the students should have acquired:
  • Knowledge about common statistical tools for biometrics
  • Insight into advantages and disadvantages of biometric characteristics
  • Understanding of multimodal biometrics
  • Knowledge of ethical and privacy issues in biometrics.
  • Understanding of the threats and protection mechanisms for biometric data
  • The ability to choose an appropriate biometric method for a given application area.
Content:In this course, several key aspects of biometrics are covered.


The lecture begins with an overview of applied statistics and hypothesis tests as well as other common statistical tools for biometrics, and then covers selected biometric concepts, particularly fingerprint recognition, vein recognition, face recognition and iris recognition. To this end, the relevant physiological characteristics, their variability, and potential problems are discussed before analyzing different approaches for each of the attributes to be investigated. In each case, not only benign applications are covered but also potential bottlenecks such as insufficient sample quality along the entire processing chain. The use of multi-biometrics including data fusion is discussed both in the context of robustness against attacks and improving the overall accuracy of the recognition process. The course continues with a discussion of the ethical and privacy-related issues in biometrics, along with possible limitations and technical mitigation mechanisms. Special attention is given to privacy enhancing technologies that provides protection of sensitive biometric data. In this line the course concludes with comparison-on-card approaches and template protection concepts that allow revocation of biometric references.


The seminar will complement the topics of the lecture. The seminar will investigate application scenarios of biometrics in more detail. Further the student will have a chance to interact with current research projects. The student will provide a research report (term paper) on a topic that is chosen by the student in coordination with the lecturer.
  • S. Li , A.K. Jain, Handbook of Face Recognition, Springer, (2011)
  • D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, Springer, (2009).
  • J. Wayman, A. Jain, D. Maltoni, D. Maio, Biometric Systems, Springer, (2005).
Responsibility:Christoph Busch
Released:SS 2022
Offered in SS 22:Busch
Professional competencies:
  • formal, algorithmic, mathematical competencies: medium
  • analytical, design and implementation competencies: high
  • technological competencies: medium
  • capability for scientific work: medium
Interdisciplinary competencies:
  • project related competencies: low

[Fachbereich Informatik] [Hochschule Darmstadt]
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