Cooperative representation of multiscale patch face recognition based on fuzzy decision

The machine learning of small sample size is one of the most challenging problems in face recognition. Multiscale patch cooperative representation for face recognition provides multiple patch scales to a sample set. In each patch scale, all samples in the training set and test set are segmented into patches of the same size, followed by a collaborative representation classification. A sample belongs to the category in which the number of patch blocks is the highest. Under a single scale, however, the patch blocks of a sample often belong to different categories. If the decision value of the sample is defined as the category with the largest number of patches, the possibility of the sample belonging to other categories is often disregarded; this will then significantly reduce the recognition accuracy. Therefore, a multiscale patched collaborative representation based on fuzzy decision is proposed herein. In a single scale, among all the patch blocks in a sample, the proportion of patch blocks belonging to a category to the total patch blocks is used to represent the degree of a sample belonging to the category. Hence, a fuzzy decision matrix can be obtained for a sample set in each scale. The elements of the decision matrix represent the possibility of samples belonging to categories, thereby solving the absolute problem of classification. Corresponding weights are applied to the patch scales, and multiscale outputs can be integrated by regularizing boundary distribution optimization. It is experimentally demonstrated that the proposed method exhibits high recognition accuracy and is superior to some existing algorithms.