Face recognition using direct LPP algorithm

Amounts of data under varying intrinsic features are empirically thought of as high dimensional nonlinear manifold in the observation space. Locality preserving projections (LPP) is a linear transform that optimally preserves the local structure of the data set, and explicitly considers the manifold structure modeled by an adjacency graph. LPP has been applied in many domains successfully, however, LPP algorithm needs a PCA transform in advance to avoid a possible singular problem. Further, LPP is non-orthogonal and this makes it difficult to reconstruct the data. Orthogonal LPP (OLPP) has more discriminating power than LPP, however, the experiments imply that the robustness of OLPP should be improved. Moreover, OLPP also needs a PCA transform in advance. Using PCA as preprocessing can reduce noise, but some discriminative information also is abandoned. In this paper, we present a approach (direct LPP) to extract features from the original data set directly by solving common eigen-value problem of symmetric positive semi-definite matrix. DLPP shares the excellence of LPP and OLPP. Moreover, DLPP is least-squares normalized orthogonal, while OLPP is not known to be optimal for LPP in any sense. Experimental results demonstrate the effectiveness and robustness of our proposed algorithm.