A Regularized Locality Projection-Based Sparsity Discriminant Analysis for Face Recognition

Abstract

Manifold learning and classifiers based on sparse representation are widely used in pattern recognition. Most of the conventional manifold learning methods are subjected to the choice of parameters. In this paper, we present a Regularized Locality Projection based on Sparsity Discriminant Analysis (RLPSD) method for Feature Extraction (FE) to understand the high-dimensional data such as face images. In RLPSD, firstly, we show the sparse representation of training samples by collaborative representation-based classification (CRC). Secondly, the idea of part optimization based on sparse representation is used to ensure the within-class compactness which combines with the labels of measurements and the weights of sparse presentation can be as small as possible. Finally, whole optimization can be directly obtained without the iteration of local optimization. Meanwhile, the separability information of between-class can be well discriminated by scatter matrix which is similar to Fisher linear discriminant analysis (LDA). The great recognition performance of the proposed method is verified by comparing with the popular algorithms on Yale, ORL, AR and Extended YaleB face databases and Oxford 102 flowers dataset.