Semi-random subspace method for face recognition

Abstract

The small sample size (SSS) and the sensitivity to variations such as illumination, expression and occlusion are two challenging problems in face recognition. In this paper, we propose a novel method, called semi-random subspace (Semi-RS), to simultaneously address the two problems. Different from the traditional random subspace method (RSM) which samples features from the whole pattern feature set in a completely random way, the proposed Semi-RS randomly samples features on each local region (or a sub-image) partitioned from the original face image. More specifically, we first divide a face image into several sub-images in a deterministic way, then construct a set of base classifiers on different randomly sampled feature sets from each sub-image set, and finally combine all base classifiers for the final decision. Experimental results on five face databases (AR, Extended YALE, FERET, Yale and ORL) show that the proposed Semi-RS method is effective, relatively robust to illumination and occlusion, etc., and also suitable to slight variations in pose angle and the scenario of one training sample per person. In addition, kappa-error diagram, which is used to analyze the diversity of algorithm, reveals that Semi-RS constructs more diverse base classifiers than other methods, and also explains why Semi-RS can yield better performance than RSM and V-SpPCA.