DISCRIMINATIVE LOCAL LEARNING PROJECTION FOR FACE RECOGNITION
Subspace learning method has commonly been used as a popular way to understand high dimensional data such as face images. In this paper, a novel subspace learning method called Discriminative Local Learning Projection (DLLP) is proposed for face recognition. By characterizing the local structures and dissimilarities between the supervised data manifolds, a linear transformation that can maximize the dissimilarities between all manifolds and simultaneously minimize the local estimation error can be computed. Thus the proposed algorithm embeds the discriminative information as well as the local geometry of samples into the objective function. And the abilities of preserving the local structure in each manifold and classification are both combined into the algorithm. Extensive experiments on face databases demonstrate the effectiveness of DLLP.
discriminative local learning projection subspace learning manifolds locality preserving