Abstract
Despite the existence of many biometric systems such as hand geometry, iris scan, retinal scanning and fingerprints, the face recognition will remain a powerful tool due to many advantages such as his low cost, the absence of physical contact between user and biometric system, and his user acceptance. Thus, a large number of face recognition approaches has been lately done. In this paper, we present a new 2D face recognition approach called HMM-LBP permitting the classification of a 2D face image by using the LBP tool (Local Binary Pattern) for feature extraction. It is composed of four steps. First, we decompose our face image into blocs. Then, we extract image features using LBP. Next, we calculate probabilities. Finally, we select the maximum probability. The obtained results were presented to prove the efficiency and performance of the novel technique.