Discriminative Dictionary Learning with Local Constraints for Face Recognition with Occlusion

Abstract

Facial occlusion, such as sunglasses, mask etc., is one important factor that affects the accuracy of face recognition. Unfortunately, faces with occlusion are quite common in the real world. In recent years, sparse coding becomes a hotspot of dealing with face recognition problem under different illuminations. The basic idea of sparse representation-based classification is a general classification scheme in which the training samples of all classes were taken as the dictionary to represent the query face image, and classified it by evaluating which class leads to the minimal reconstruction error of it. However, how to balance the shared part and class-specific part in the learned dictionary is not a trivial task. In this paper we make two contributions: (i) we present a new occlusion detection method by introducing sparse representation-based classification model; (ii) we propose a new sparse model which incorporates the representation-constrained term and the coefficients incoherence term. Experiments on benchmark face databases demonstrate the effectiveness and robustness of our method, which outperforms state-of-the-art methods.