A Weighted Sparse Neighbourhood-Preserving Projections for Face Recognition

Abstract

ABSTRACTDimensionality reduction algorithms are widely applied to high-dimensional data pre-processing, especially for face images. In this paper, we propose an unsupervised sparse subspace learning approach called weighted sparse neighbourhood-preserving projections (WSNPP) for face recognition. Unlike many existing approaches such as sparsity-preserving projections (SPP), where the constructive weights are computed by the classical sparse representation (SR), WSNPP utilizes a weighted SR model to represent samples. The obtained projections can contain more local discriminant information than classical sparse subspace learning methods. Moreover, WSNPP puts a constraint on the number of nonzero reconstruction coefficients and hence is more robust to global noises and time saving. Experiments on AR, Yale-B and ORL image datasets demonstrate its effectiveness.