Label information-based weighted regularized sparsity preserving embedding for face recognition

Graph learning framework has become a popular method of dimensional reduction. However, the traditional graph construction heavily relies on the selection of parameters, resulting in unstable performance in real-world face recognition applications. To address this, a label information-based weighted regularized sparsity preserving embedding for face recognition is proposed in this paper. Different from the existing L1-graph, we adaptively construct both intrinsic graph and penalty graph with label information-based L1-graph in the graph embedding framework. In order to preserve the local structure, Gaussian kernel distances between the samples are used as weight matrix to weight graph. In addition, the problem of irreversible matrix is alleviated by regularization instead of PCA that loses some discrimination information. At last, an objective function combining globality and locality is created to reduce dimensionality. Meanwhile, Schmidt orthogonalization is used to obtain the orthogonal basis vectors. The experimental results on public face database illustrate that the proposed algorithm has high recognition rate.