A LBP-based Face Recognition Method with Hamming Distance Constraint

In this paper, we present a new LBP-based face recognition method with Hamming distance constraint. The traditional LBP operator uses a uniform pattern to describe the local features and assort the other nonuniform patterns to one additional class, for images under expression and illumination condition changes, this method could cause more inaccuracy and instability. By assuming that the illumination, pose or expression changes of a face image are some kinds of "noise", we introduce the widely used Hamming distance in channel coding to LBP so as to decrease the error rate caused by these noise disturbances. Experimental results on FRGC show that our method improves the recognition performance obviously than the traditional LBP-based face recognition methods when face images are under uncontrolled circumstances.