Combined local features selection for face recognition based on Naïve Bayesian classification

Face recognition is a very popular biometric solution in the literature. Several solutions are presented to meet the needs of individual´s verification or identification. There are three types of face recognition approaches: local, global and hybrid. In this paper, we proposed a local approach for face recognition based on combined features selection methods like Genetic algorithm, Gramdt Shmidt algorithm, mRmR features selection algorithm and naïve Bayesian classifier. Our proposed approach will be compared with some face recognition systems based on global features. A comparative study is given in this paper based on Recognition rates and Execution times. Our Face recognition system, which is based on naïve Bayesian classifier and tested on ORL face database, has showed 78.75% recognition rate and interesting execution times compared to global approaches.