Locality preserving discriminant projections for face and palmprint recognition

Abstract

A new subspace learning algorithm called locality preserving discriminant projections (LPDP) is proposed by adding the criterion of maximum margin criterion (MMC) into the objective function of locality preserving projections (LPP). LPDP retains the locality preserving characteristic of LPP and utilizes the global discriminative structures obtained from MMC, which can maximize the between-class distance and minimize the within-class distance. Thus, our proposed LPDP combining manifold criterion and Fisher criterion has more discriminanting power, and is more suitable for recognition tasks than LPP, which considers only the local information for classification tasks. Moreover, two kinds of tensorized (multilinear) forms of LPDP are also derived in this paper. One is iterative while the other is non-iterative. The proposed LPDP method is applied to face and palmprint biometrics and is examined using the Yale and ORL face image databases, as well as the PolyU palmprint database. Experimental results demonstrate the effectiveness of the proposed LPDP method.