Seminar in Biometric Systems

Study program: Electrical engineering, 2nd Bologna Cycle
Type: Elective
Semester: 2 year
Credits: 6

Lecturer: Assoc. Prof. Vitomir Štruc, PhD

Aims of the course

The course aims at introducing the basic principles and components of biometric systems aswell as at presenting examples of biometric systems for the automated recognition of people.


Basic knowledge of Artificial Intelligent Systems, Pattern Recognition, Information Theory, and Computer Vision is recommended

Content (Syllabus outline)

  • Introduction to Biometric Systems: identifiable biometric characteristics (physiological, behavioural), system components and stages of operation (enrolment, verification, identification).
  • Acquisition of Physiological (face, fingerprints, iris, hand palms and geometry) and Behavioural (voice, handwriting, and gait) Characteristics: contact and contactless measurement, frequently used sensors. Testing the quality and genuineness of acquired data.
  • Design of Uni-modal and Multi-modal Biometric Systems: sources of biometric information, levels and methods of biometric information fusion. Comparison of uni- and multi-modal systems.
  • Evaluation of Biometric Systems: average enrolment and recognition time, biometric system errors (matching and decision errors), enrolment error, data acquisition error.
  • Testing of Biometric Systems: test plan, person group, testing enrolment, verification and identification processes. Forgery tests. Databases for automated and repeatable tests.
  • Biometric Standards and Privacy Issues. Ethical and Cultural Issues associated with biometric system applications.
  • Seminars: development of uni- and multi-modal biometric systems: biometric systems in security (identification and travel documents, e-commerce, e-security systems) and others (smart rooms and environments, user-adapted content search) applications.


Students produce a seminar in the scope of the course. Below are a few examples from past years:

  • T. Kambič, Multi-task learning for joint face recognition and presentation attack detection, SBS seminar, 2018 [PDF]
  • M. Kastelic, Using Domain-Knowledge for Correlation-based Face Tracking, SBS seminar, 2018 [PDF]
  • M. Pernuš, 3-D Face Landmark Detection using Multitask Hourglass Model, SBS seminar, 2017 [PDF]
  • S. Fabijan, Cross-Resolution Face Recognition using Quintuplet Metric Learning, SBS seminar, 2017 [PDF]
  • J. Cubelos Ordas, Face Presentation Attack Detection using Nuisance Attribute Projection, SBS seminar, 2017 [PDF]

Recommended readings

  • Jain, A. A. Ross, K. Nandakumar, Introduction to Biometrics, Springer, 2011.
  • Theodoridis, K. Koutroumbas: Pattern Recognition, Fourth Edition,
    Academic Press, 2008.
  • Wayman, A. K. Jain, D. Maltoni, D. Maio (eds.): Biometric Systems : Technology, Design and Performance Evaluation, Springer, 2004.

Intended learning outcomes

After completing this course, the student will be able to demonstrate a knowledge and understanding of the:

  • principles of the construction of biometric systems for the automated recognition of people,
  • problems of quality assurance in biometric systems and the protection of biometric data,
  • ethical and cultural issues associated with the use of biometric systems.

The use of knowledge:

The student will be able to use the acquired knowledge to develop and construct biometric systems for the automated recognition of people (identification and travel documents, e-commerce, e-security systems, smart rooms and environments, user-adapted multi-media content search, identification of the writers of historical documents, criminal investigations support).

Transferable skills:

  • the use of literature and other resources in the field of biometric systems;
  • the use of open source development tools, data sets and programming environments: the students carry out the projects in one of the programming languages C/C++, Python, C#, Java, or using MATLAB, use one of the biometric databases (NIST SRE, XM2VTS, FRGC, Banca, LFW, PolyU, etc.), and use tools like OpenCV, ORANGE and WEKA;
  • communication skills: oral presentation of seminar projects, preparing seminar project reports;
  • problem solving: problem analysis, algorithm design, implementation and testing of a program;
  • group work: the organization and management of groups, active participation in groups.


More information on the course is available in the E-classroom.