Explore semantic pixel sets based local patterns with information entropy for face recognition

Several methods have been proposed to describe face images in order to recognize them automatically. Local methods based on spatial histograms of local patterns (or operators) are among the best-performing ones. In this paper, a new method that allows to obtain more robust histograms of local patterns by using a more discriminative spatial division strategy is proposed. Spatial histograms are obtained from regions clustered according to the semantic pixel relations, making better use of the spatial information. Here, a simple rule is used, in which pixels in an image patch are clustered by sorting their intensity values. By exploring the information entropy on image patches, the number of sets on each of them is learned. Besides, Principal Component Analysis with a Whitening process is applied for the final feature vector dimension reduction, making the representation more compact and discriminative. The proposed division strategy is invariant to monotonic grayscale changes, and shows to be particularly useful when there are large expression variations on the faces. The method is evaluated on three widely used face recognition databases: AR, FERET and LFW, with the very popular LBP operator and some of its extensions. Experimental results show that the proposal not only outperforms those methods that use the same local patterns with the traditional division, but also some of the best-performing state-of-the-art methods.