Matrix Exponential LPP for face recognition

Abstract

Face recognition plays a important role in computer vision. Recent researches show that high dimensional face images lie on or close to a low dimensional manifold. LPP is a widely used manifold reduced dimensionality technique. But it suffers two problem: (1) Small Sample Size problem; (2)the performance is sensitive to the neighborhood size k. In order to address the problems, this paper proposed a Matrix Exponential LPP. To void the singular matrix, the proposed algorithm introduced the matrix exponential to obtain more valuable information for LPP. The experiments were conducted on two face database, Yale and Georgia Tech. And the results proved the performances of the proposed algorithm was better than that of LPP.