Pattern recognition and biometrics

Catalogue des cours de Télécom SudParis

Code

IGSF IMA 4511

Niveau

M1

Graduate

Graduate

Semestre

Spring

Domaine

Image

Programme

Programme Ingénieur

Langue

Anglais/English

Crédits ECTS

4

Heures programmées

45

Charge de travail

90

Coordonnateur(s)

Département

  • Electronique et Physique

Equipe pédagogique

Organisation

Cours/TD/TP/projet/examen : 18/0/24/3

Acquis d'apprentissage

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

Compétences CDIO

  • 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

Prérequis

Notions of Statistics and Probability Theory (Course "Introduction aux statistiques")

Mots-clés

Biometrics, face recognition, on-line signature verification, iris recognition, gait recognition, speaker verification

Contenu

First part : Basics of Pattern Recognition
Introduction
Bayes Classifier
The K Nearest Neighbor Rule
Hidden Markov Models
Principal Component Analysis, Discriminant Analysis

Second part : Application to Biometric Identity Verification
Face Recognition Techniques
On-line Signature Verification Techniques
Iris Recognition Techniques
Gait Recognition Techniques
Speaker verification Techniques

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]

Formule de l'évaluation

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]

Fiche mise à jour le 30/10/2018