Biometric Technologies for Information Security

Biometric Technologies for Information Security

Faculty of Engineering
Ph.D. PROGRAM In Electronic and Computer Science Engineering


Lecturer: Prof. Gian Luca Marcialis - marcialis[at]diee[dot]unica[dot]it

Language: Italian, but the whole seminary can be given in English according to the students' needs.

Target students: B.Sc. in Biomedical Engineering and Electrical, Electronic Eng. and Computer Science. M.Sc. and Ph.D. students are invited to follow the course Biometric Technologies and Behavioural Security.

Last update on jan., 9th, 2020 


Goal of the course.
This series of lectures is aimed to introduce the fundamentals of biometric technologies for personal recognition.

A biometric system for personal verification is made up of the following modules: sensor, feature extractor, matcher. These modules will be described with respect to two biometrics: fingerprint and facial traits. Multi-modal biometrics, that is, systems which combine information from multiple biometrics, will be also presented. Finally, some open research issues will be introduced. Scientific and technological issues, and a focus on the pros and cons of each biometric system, will be discussed. The course can be considered as a follow up of the “Machine Learning” course, since biometrics are a particular application of the concepts explained in that course. Students will visit the “Fingerprint Liveness Detection” Laboratory where they could use some proof-of-concept systems developed by the PRA Lab. Some invited speakers from public and private companies complete the topics treated by the seminary.
Fundamentals of statistical pattern recognition.
  • 4 hours – Statistical and pattern recognition foundamentals
  • 4 hours – Fingerprint
  • 4 hours – Faces
  • 2 hours – Multimodal biometrics
  • 1 hour – Other biometrics: iris, palmvein, gait, voice, signature.
  • 1 hour  – Foundamentals of behavioural analysis and Brain-Computer Interfaces.
  • 8 hours – Invited speakers.


  1. Introduction. Motivation and potentialities of biometric systems. Personal verification and recognition.
  2. Pattern recognition. Sensoring. Feature extraction, processing. Classification. Performance parameters. Hypothesis verification tests.
  3. Fingerprints. Definition. Sensoring. Feature extraction. Matching algorithms. Vulnerabilities. Fake fingerprint detection.
  4. Faces. Definition. Sensoring. Feature extraction. Matching algorithms.
  5. Multimodal biometrics. Definition. Fusion algorithms: feature-level and match score-level.
  6. Other biometrics. Features and matching steps for iris, palmvein, gait, signature and voice.
  7. Emerging applications. Behavioural analysis and BCIs.
  8. Invited speakers.
Slides of the previous academic year can be downloaded at this link.
Credits:  with final test/B.Sc thesis 2


Important Dates (2020):

Each lesson will take 4 hours :
  • February, 3-4-5, 12-13-14th, 2-6pm, AB room (IAI_IA), I building.