Hochschule Darmstadt - Fb Informatik

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Module Description
Module:Biometric Systems
Course numbers:41.4980 [PVL 41.4981; Module 41.49800]
Study programme:Dualer Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
Dualer Master 2013 - Vertiefung IS: IT-Sicherheit
JIM 2013 - Elective Catalogue J
Master 2013 - Katalog AS: Anwendungs- und systemorientierte Module
Master 2013 - Vertiefung IS: IT-Sicherheit
JIM 2006 - Courses
Master 2006 - Katalog AS: Anwendungs- und systemorientierte Module
Master 2006 - Vertiefung CG: Computer Graphik
Master 2006 - Vertiefung IS: IT-Sicherheit
Master 2006 - Vertiefung TK: Telekommunikation
Master 2006 - Vertiefung TS: Technische Systeme
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 (2 hours)
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 oft he findings. The presentation will take place in the seminar.)
Proportion of PVL:33%
Frequency of offering:each summer semester (lastly in SS 2017)
Goal: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).
Lecture style / Teaching aids:Slides and board will be used in the lecture. Further there is a set of short video. Students will be provided with a copy of the slides and with additional reading material on the topics of the lecture.
Special team:IT-Sicherheit
Responsibility:Christoph Busch
Approval:SS 2015
Offered in SS 17:Busch
Professional competencies:
  • formal, algorithmic, mathematical competencies: medium
  • analytical, design and implementation competencies: high
  • technological competencies: medium (technology of biometrics sensors, signal processing, feature extration, privacy enhancing technologies)
  • capability for scientific work: high
Interdisciplinary competencies:
  • project related competencies: medium
  • interdisciplinary expertise: basic technical and natural scientific competence, basic juristic competence
  • social and self-competencies: ability to work in a team, analytical competence, judging competence, competence of knowledge acquisition, presentational, documentary, teaching and mentoring competence, fluency

[Fachbereich Informatik] [Hochschule Darmstadt]
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