A Simulated Annealing and 2DPCA Based Method for Face Recognition

Abstract

In this paper we address the problem of face recognition based on two-dimensional principal component analysis (2DPCA). The similarity measure plays an important role in pattern recognition. However, with reference to the 2DPCA based method for face recognition, studies on similarity measures are quite few. We propose a new method to identify the similarity measure by simulated annealing (SA), which is called SA similarity measure. Experimental results on two famous face databases show that the proposed method outperforms the state of the art methods in terms of recognition accuracy.

Comparative Study of Local Binary Pattern Derivatives for Low Size Feature Vector Representation in Face Recognition

“…The feature extraction process plays the crucial role in face recognition. There are many feature extraction methods based on geometry [1,2], statistics [3,4] or texture analysis [5,6] which have been proposed and used in face recognition systems. The performance and accuracy of those methods may vary, however, due to varied illumination, facial expression and pose.…”