Supervised kernel locality preserving projections for face recognition

Subspace analysis is an effective approach for face recognition. Finding a suitable low-dimensional subspace is a key step of subspace analysis, for it has a direct effect on recognition performance. In this paper, a novel subspace method, named supervised kernel locality preserving projections (SKLPP), is proposed for face recognition, in which geometric relations are preserved according to prior class-label information and complex nonlinear variations of real face images are represented by nonlinear kernel mapping. SKLPP cannot only gain a perfect approximation of face manifold, but also enhance local within-class relations. Experimental results show that the proposed method can improve face recognition performance.