The effect of the similarity measures and the interpolation techniques on fractional eigenfaces algorithm

Face recognition system is considered as a smart technique for authentication. It guarantees security, stability and variability. It was used in a wide variety of applications like control of access, surveillance, passport and credit cards. Many algorithms were proposed in order to improve the recognition rate. One of these techniques is the fractional Eigenfaces, which combines the Eigenfaces algorithm and the theory of the fractional covariance matrix. In this paper, we highlight the influence of the interpolation and the similarity measurement methods on the efficiency of the fractional Eigenfaces algorithm. Experimental results are evaluated with three image databases: ORL, YALE and UMIST.