Fisher Discriminative Least Square Regression with Self-Adaptive Weighting for Face Recognition

As a supervised classification method, least square regression (LSR) has shown promising performance in multiclass face recognition tasks. However, the latest LSR based classification methods mainly focus on learning a relaxed regression target to replace traditional zero-one label matrix while ignoring the discriminability of transformed features. Based on the assumption that the transformed features of samples from the same class have similar structure while those of samples from different classes are uncorrelated, in this paper we propose a novel discriminative LSR method based on the Fisher discrimination criterion (FDLSR), where the projected features have small within-class scatter and large inter-class scatter simultaneously. Moreover, different from other methods, we explore relax regression from the view of transformed features rather than the regression targets. Specifically, we impose a dynamic non-negative weight matrix on the transformed features to enlarge the margin between the true and the false classes by self-adaptively assigning appropriate weights to different features. Above two factors can encourage the learned transformation for regression to be more discriminative and thus achieving better classification performance. Extensive experiments on various databases demonstrate that the proposed FDLSR method achieves superior performance to other state-of-the-art LSR based classification methods.