Multiscale collaborative representation for face recognition via class-information fusion

Abstract

One of the most challenging issues in face recognition is having only a limited number of training images. Multiscale patch collaborative representation (MSPCRC) is an effective approach to address this problem. However, the existing MSPCRC methods only defined a single patch-scale weight vector for all classes to indicate the importance of different patch scales, ignoring the role of class information when fusing multiscale recognition results. In this work, we consider the effect of class information on face recognition and propose a novel multiscale collaborative-representation face recognition method. Specifically, we first construct the multiscale decision matrices of image subsets from different classes according to patch collaborative representation, and define a patch-scale weight vector for each class to describe the importance of different patch scales. Each element in a scale-weight vector represents the weight value of a certain scale in the corresponding class. Then, we construct the respective optimization objective function for each class, which takes into account the classification information both from the same class and from different classes. Finally, we propose a multiscale fusion-recognition output rule based on the patch-weight vectors. Experimental results demonstrate that the proposed method enhances classification accuracy by approximately 2% to 5% across multiple datasets, surpassing the majority of the competing methods.