Développement et Évaluation d'un Système de Reconnaissance Faciale Basé sur le Classificateur Deep Rule-Based (DRB)

Abstract

Facial recognition systems have become one of the most widely used systems in the fields of security and surveillance. Despite significant technological advancements, their performance is still affected by changes in shooting conditions, such as lighting and modifications to facial features caused by aging or different facial expressions. This thesis aims to improve the performance of facial recognition systems using a new DRB classifier. The proposed solutions have led to significant improvements compared to other classifiers (NN classifier, SVM). Image matching descriptors Gabor, LPQ, MBC, and IWBC were used in experiments applied to the ORL, 15 Yale, Face94, Face95, Face96, and Jaffe databases, which are among the most used in academic studies to compare the results and determine the degree of improvement achieved