Over the past thirty years, face recognition has been an active area of ​​research in computer vision and one of the most successful applications of image analysis and understanding. Different algorithms have been proposed by researchers to solve this problem and many of them are considered state of the art. Nowadays, the problem of facial recognition is not only important in the research field, but it is vital in commercial applications. A general statement of the problem of face recognition can be stated as follows: given still or video images of a scene, identify or verify one or more people in the scene using a stored database of faces . Due to this definition, well-known algorithms such as PCA, ICA, LDA, EBGM, Bayesian Framework, HMM, SVM and Boosting are proposed by Turk et al. [1], Bartlett et al. [2], Zhao et al. [3], Wiskott et al. [4], Moghaddam et al. [5], Néfian et al. [6], Heisele et al. [7], and Lu et al. [8].These algorithms have acceptable success on test image databases. However, the use of these algorithms in commercial applications is subject to certain conditions....