2011-2012 - IMT4621 - 5 ECTS



On the basis of

The course content will be complementary to the course IMT4721 "Authentication".

Expected learning outcomes


The candidate possesses advanced knowledge in Biometrics.

The candidate possesses thorough knowledge about theory and scientific methods relevant for design, development and operation of biometric access control systems.

The candidate is capable of applying his/her knowledge in new fields of IT-security systems.


The candidate is capable of analyzing existing theories, methods and interpretations in the field of biometrics and working independently on solving theoretical and practical problems.

The candidate can use relevant scientific methods in independent research and development in biometrics.

The candidate is capable of performing critical analysis of various literature sources and applying them in structuring and formulating scientific reasoning in biometrics.

The candidate is capable of carrying out an independent limited research or development project in biometrics under supervision, following the applicable ethical rules.

General competence

The candidate is capable of analyzing relevant professional and research ethical problems in biometrics.

The candidate is capable of applying his/her biometric knowledge and skills in new fields, in order to accomplish advanced tasks and projects.

The candidate can work independently and is familiar with biometric terminology.

The candidate is capable of discussing professional problems, analyses and conclusions in the field of biometrics, both with specialists and with general audience.

The candidate is capable of contributing to innovation and innovation processes.


After the course, the students should have acquired:

1. Knowledge about common statistical tools for biometrics

2. Insight into advantages and disadvantages of biometric characteristics

3. Understanding of multimodal biometrics

4. Knowledge of ethical and privacy issues in biometrics.

5. Understanding of the threats and protection mechanisms for biometric data


• Fingerprint recognition

• Vein recognition

• Face recognition specifically focused on three dimensional data

• Iris recognition

• Multimodal biometrics

• Attack mechanisms

• Privacy Enhancing Technologies


In this course, several key aspects of biometrics are covered. The course 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.

Teaching Methods


Teaching Methods (additional text)

Tutorial: Afternoon sessions with seminar discussion and practical tasks

Form(s) of Assessment

Written exam, 3 hours

Form(s) of Assessment (additional text)

Written examination in English

Grading Scale

Alphabetical Scale, A(best) – F (fail)

External/internal examiner

Evaluated by an external examiner.

Re-sit examination

Ordinary re-sit examination.

Examination support

Dictionaries allowed (no calculator)

Coursework Requirements

Students can contribute a research report (term paper) on a topic that is chosen by the student in coordination with the lecturer, which will be considered for the assessment

Teaching Materials

Recommended literature:

[1] LI , S . Z. , AND JAIN, A. K. , Eds. Handbook of Face Recognition. Springer-Verlag,

Heidelberg, Germany, 2005.

[2] MALTONI , D. , MAIO, D. , JAIN, A. K. , AND PRABHAKAR , S . Handbook of Fingerprint Recognition. Springer-Verlag, Heidelberg, Germany, 2005.

[3] WAYMAN, J . , JAIN, A. , MALTONI , D. , AND MAI O, D. , Biometric Systems.

Springer-Verlag, Heidelberg, Germany, 2005.

[4] JAIN, L.C. , HALICI, U. , HAYASHI, I. ; LEE, S.B., TSUTSUI, S. Intelligent Biometric Techniques in Fingerprint and Face Recognition. CRC PressVerlag, 1999.

[5] TUYLS, P., SKORIC, B., KEVENAAR, T.  Security with Noisy Data. Springer-Verlag, 2007

Additional information

In case there will be less than 5 students that will apply for the course, it will be at the discretion of Studieprogramansvarlig whether the course will be offered or not an if yes, in which form.