Code
IGSF IMA 4511
Level
M1
Graduate
Graduate
Semester
Spring
Domain
Image
Program
Programme Ingénieur
Language
Anglais/English
ECTS Credits
4
Class hours
45
Workload
90
Program Manager(s)
Department
- Electronique et Physique
Educational team
Organisation
Cours/TD/TP/projet/examen : 18/0/24/3Learning objectives
By the end of this course, second year students will be able to:
- master the tools for pattern recognition and data classification
- know the specific techniques of the different biometric modalities in terms of the general tool adaptation to each of them
- to be able to implement a biometric system of identity verification
CDIO Skills
- 1.1.1 - Mathematics (including statistics)
- 2.1.2 - Modeling
- 2.1.3 - Estimation and Qualitative Analysis
- 3.2.3 - Written Communication
- 3.3.1 - Communications in English
Prerequisites
Notions of Statistics and Probability Theory (Course "Introduction aux statistiques")
Keywords
Biometrics, face recognition, on-line signature verification, iris recognition, gait recognition, speaker verification
Content
First Part: Basics of Pattern Recognition
Bayes Classifier
The K Nearest Neighbour Rule
Unsupervised Learning and Clustering
Hidden Markov Models
Principal Component Analysis, Discriminant Analysis
Second Part: Biometrics and other applications
Verification, Identification, Re-identification
Performance measures of a Biometric System
Gait Recognition Techniques
Speaker Verification Techniques
On-line Signature Verification Techniques
Iris Recognition Techniques
Face Recognition Techniques
Gesture recognition
Action Recognition in Video Sequences
Evaluation
Validation is based on 3 evaluated Lab sessions (Lab1, Lab2 and Lab3) and an written exam (W).
Final Mark = 1/2 [Average (Lab1, Lab2, Lab3) + W]
Assessment formula
Validation is based on 3 evaluated Lab sessions (Lab1, Lab2 and Lab3) and an written exam (W).
Final Mark = 1/2 [Average (Lab1, Lab2, Lab3) + W]