Non-negative Sparse Discriminative Low Rank Preserving Projection for Robust Face Feature Extraction

Abstract

Face recognition is an important aspect of intelligent security for new energy vehicles. Existing methods extract features without taking low-rank and discriminative similarity relations of data into account and resulting in low quality feature distribution. In addition, the coefficients of the learned representation matrix can be negative and lack interpretability. To address the above issues, a method named non-negative sparse discriminative low rank preserving (NNSDLRPP) which introduces sparsity, non-negativity and block-diagonal regularization is proposed. As a result, NNSDLRPP improves the interpretability of representation features while capturing discriminative information of data. Extensive experiments on two face datasets show that the proposed method outperforms other state-of-the-art methods.