Fuzzy Linear Regression Discriminant Projection for Face Recognition

Abstract

How to capture distinctive features from facial images when there are large variations in illumination, poses, and expressions is important for the face recognition problems. This paper introduces a novel algorithm called fuzzy linear regression discriminant projection (FLRDP) for face recognition. The proposed algorithm FLRDP seeks to generate an efficient subspace for the LRC method and could effectively handle variations between facial images. To be specific, FLRDP first computes the gradual membership degrees of each sample to corresponding classes, and then incorporates such membership degree information into the construction of the fuzzy between-class and within-class reconstruction errors. Finally, the criterion function is derived via maximizing the ratio of the fuzzy between-class reconstruction error to the fuzzy within-class reconstruction error. Experimental results carried out on the ORL, CMU PIE, and FERET face databases show the superiority of our proposed method over other state-of-the-art algorithms.