Completed local similarity pattern for color image recognition

Abstract

In this paper, we propose a simple yet effective local color image descriptor, completed local similarity pattern (CLSP), for face recognition. We represent the color image as the co-occurrence of its image pixel color quantization information and the local color image textural information. Specifically, CLSP consists of two complementary components: color label and local similarity pattern. Expressed by soft color label for every image pixel, we adopt the k-means clustering method and the soft-assignment coding method to summarize and encode the image pixel color information globally ignoring local neighboring pixels. While based on the similarity of color information between central pixel and its neighbors, local similarity pattern (LSP) is used to encode the local spatial textural feature of the color image ignoring their color value. Therefore, LSP has the merits of robustness and compactness of coding based feature extraction method. The joint distribution (2-D histogram), which unifies the color label and LSP, is used for a given image representation. Moreover, seven different color spaces are selected and fused to compensate each other for color image feature extraction. Experimental results on GeorgiaTech, AR, NUST-RWFR and LFW face database show that CLSP outperforms state-of-the-art color image feature extraction methods.