Occluded Face Recognition with a Novel Model Incorporating Block Diagonal Structure and Feature Consistency

Abstract

The nuclear norm matrix regression method is effective for continuous occlusion in face recognition. However, the existing method only considers low-rank structural information and ignores correlation between sample image representations. To effectively solve these issues, we propose a novel occluded face recognition model. The model enhances differences between categories using a strict 0–1 block diagonal structure. It also improves feature representation consistency within the same category with a local preservation term. The introduction of these two terms enables the model to obtain more discriminative representation coefficients. The experimental results on the Extended Yale B, AR, and LFW databases demonstrate that the proposed method has better recognition performance for occluded face recognition than comparative methods.