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BIOMETRY AND BEHAVIOR ANALYSIS

International Teaching BIOMETRY AND BEHAVIOR ANALYSIS

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0622700132
DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS
EQF7
COMPUTER ENGINEERING
2025/2026



YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2022
AUTUMN SEMESTER
CFUHOURSACTIVITY
324LESSONS
324LAB
- T
Objectives
THE COURSE IS AIMED AT PROVIDING THE STUDENT WITH THE THEORETICAL, METHODOLOGICAL AND TECHNOLOGICAL KNOWLEDGE ON BIOMETRIC AUTHENTICATION SYSTEMS, INCLUDING DATA ACQUISITION, FEATURE EXTRACTION, SYSTEM ARCHITECTURE, SECURITY, AND INNOVATIVE PARADIGMS SUCH AS MULTIMODAL BIOMETRICS AND SOFT BIOMETRIC.

KNOWLEDGE AND UNDERSTANDING

TECHNOLOGIES AND ARCHITECTURES OF AUTOMATIC BIOMETRIC RECOGNITION SYSTEMS. CHARACTERISTICS, ADVANTAGES AND LIMITATIONS OF THE MOST COMMON BIOMETRIC TRAITS (FINGERPRINTS, FACE, IRIS, VOICE). FUNDAMENTALS OF MULTIMODAL BIOMETRICS, SOFT BIOMETRIC, BEHAVIOR ANALYSIS, AND CROWD ANOMALY DETECTION. SECURITY ISSUES SUCH AS SPOOFING AND DEEPFAKE ATTACKS.

APPLYING KNOWLEDGE AND UNDERSTANDING

DESIGN AND IMPLEMENTATION OF BIOMETRIC SYSTEMS THROUGH THE CONFIGURATION OF SENSORS, FEATURE EXTRACTION MODULES, AND MATCHING STRATEGIES. INTEGRATION OF SOFT BIOMETRIC FEATURES AND SECURITY MODULES INTO COMPLETE SYSTEMS. PRACTICAL EVALUATION OF PERFORMANCE AND ROBUSTNESS TO ATTACKS AND OPERATIONAL CONSTRAINTS.
Prerequisites
THE COURSE REQUIRES BASIC KNOWLEDGE OF THE PYTHON PROGRAMMING LANGUAGE, METHODS AND TECHNOLOGIES OF MACHINE LEARNING AND ARTIFICIAL VISION.
Contents
Teaching Unit 1: Biometric Authentication Systems (LECTURE/EXERCISE/LAB HOURS: 6/0/2)
- 1 (2 HOURS LECTURE): Introduction to biometric technologies.
- 2 (2 HOURS LECTURE): Architecture of a biometric recognition system.
- 3 (2 HOURS LECTURE): Introduction to multimodal biometric systems.
- 4 (2 HOURS LAB): Implementation of a biometric recognition system.
KNOWLEDGE AND UNDERSTANDING:
Understanding the core principles and technologies behind biometric authentication systems. Ability to describe the components and workflow of automatic biometric systems, recognizing their role in ensuring identity verification and their relevance in modern security contexts.
APPLIED KNOWLEDGE AND SKILLS:
Ability to configure and manage a biometric system by selecting and integrating its key modules according to the application scenario and performance requirements.

Teaching Unit 2: Biometric Traits – Data Acquisition, Feature Extraction, User Enrollment and Recognition (LECTURE/EXERCISE/LAB HOURS: 14/8/6)
- 5 (2 HOURS LECTURE): Fingerprints and palmprints: sensors and features.
- 6 (2 HOURS EXERCISE): Implementation of a fingerprint/palmprint recognition system.
- 7 (2 HOURS LECTURE): Iris recognition: data acquisition and feature extraction.
- 8 (2 HOURS EXERCISE): Implementation of an iris recognition system.
- 9 (2 HOURS LECTURE): Face recognition: features, facial expressions, occlusions.
- 10 (2 HOURS LAB): Implementation of a face recognition system.
- 11 (2 HOURS LECTURE): Speaker identification: data acquisition and features.
- 12 (2 HOURS EXERCISE): Implementation of a speaker identification system.
- 13 (2 HOURS LECTURE): Introduction to soft biometric.
- 14 (2 HOURS EXERCISE): Use of soft biometric for user identification.
- 15 (2 HOURS LECTURE): Behavior analysis and human-to-machine interaction.
- 16 (2 HOURS LAB): Algorithms for behavior analysis.
- 17 (2 HOURS LECTURE): Crowd anomaly detection.
- 18 (2 HOURS LAB): Algorithms for crowd anomaly detection.
KNOWLEDGE AND UNDERSTANDING:
In-depth understanding of commonly used biometric traits (such as fingerprints, face, iris, and voice), the corresponding acquisition devices, and feature extraction techniques. Ability to assess the strengths, weaknesses, and applicability of each trait in different operational contexts. Basic understanding of soft biometric and of behavior and crowd analysis dynamics.
APPLIED KNOWLEDGE AND SKILLS:
Ability to implement biometric systems based on widely used traits, integrate soft biometric modules, and configure systems for behavior analysis or crowd monitoring. Skills in evaluating system performance and adapting configurations to specific operational environments.

Teaching Unit 3: Security of Biometric Systems (LECTURE/EXERCISE/LAB HOURS: 4/2/2)
- 19 (2 HOURS LECTURE): Presentation attacks and spoofing detection.
- 20 (2 HOURS LAB): Implementation of an attack detection module and integration into a biometric system.
- 21 (2 HOURS LECTURE): Deepfake as a threat to face recognition systems.
- 22 (2 HOURS EXERCISE): Deepfake detection.
KNOWLEDGE AND UNDERSTANDING:
Knowledge of specific threats to biometric authentication systems, including presentation attacks and deepfake manipulation. Understanding of how these attacks compromise system reliability and the available countermeasures to mitigate such risks.
APPLIED KNOWLEDGE AND SKILLS:
Ability to develop and integrate liveness detection and attack detection modules into biometric systems to enhance their security and protect against fraudulent access attempts.

Teaching Unit 4: Project Work (LECTURE/EXERCISE/LAB HOURS: 0/0/4)
- 23 (2 HOURS LAB): Project work.
- 24 (2 HOURS LAB): Project work.
KNOWLEDGE AND UNDERSTANDING:
Ability to design a complete biometric system tailored to a specific application scenario. Skills in critically evaluating and selecting the most suitable techniques, devices, and algorithms based on technical constraints and security requirements.
APPLIED KNOWLEDGE AND SKILLS:
Practical experience in implementing a biometric system from concept to testing. Capability to validate performance through real or simulated evaluation, ensuring the solution meets operational goals.
Teaching Methods
THE COURSE CONTAINS THEORETICAL LECTURES, IN-CLASS EXERCITATIONS AND PRACTICAL LABORATORY EXERCITATIONS.
Verification of learning
THE EXAM IS COMPOSED BY THE DISCUSSION OF A TEAM PROJECTWORK (FOR 3-4 PERSONS TEAMS) AND AN ORAL INTERVIEW. THE DISCUSSION OF THE PROJECTWORK AIMS AT EVALUATING THE ABILITY TO BUILD A SIMPLE APPLICATION OF THE TOOLS PRESENTED IN THE COURSE TO A PROBLEM ASSIGNED BY THE TEACHER, AND INCLUDES A PRACTICAL DEMONSTRATION OF THE REALIZED APPLICATION, A PRESENTATION OF A QUANTITATIVE EVALUATION OF THE APPLICATION PERFORMANCE AND A DESCRIPTION OF THE TECHNICAL CHOICES INVOLVED IN ITS REALIZATION. THE INTERVIEW EVALUATES THE LEVEL OF THE KNOWLEDGE AND UNDERSTANDING OF THE THEORETICAL TOPICS, TOGETHER WITH THE EXPOSITION ABILITY OF THE CANDIDATE.
Texts
A. Jain et al., Handbook of Biometrics, Springerhttps://www.springer.com/gp/book/9780387710402

B. Bhanu and A. Kumar, Deep learning in biometrics, Springerhttps://www.springer.com/gp/book/9783319616568

C. Rathgeb et al., Handbook of Digital Face Manipulation and Detection, Springerhttps://link.springer.com/book/10.1007/978-3-030-87664-7

K. Saeed, New direction in behavioural biometrics, CRC Presshttps://www.crcpress.com/New-Directions-in-Behavioral-Biometrics/Saeed/p/book/9781498784627

V . Murino et al., Group and crowd behavior for computer vision, Academic Presshttps://www.sciencedirect.com/book/9780128092767/group-and-crowd-behavior-for-computer-vision#book-info
More Information
THE COURSE IS HELD IN ENGLISH
Lessons Timetable

  BETA VERSION Data source ESSE3