A bottom-up dictionary learning based classification for face recognition

The design of image descriptor and classifier are two important issues in face recognition. Although many facial image descriptors (e.g., subspace learning, local binary pattern) and classifier (e.g., support vector machine, sparse representation based classifier) have been proposed, these two components seldomly belong to a same framework, which may prevent the discrimination being fully exploited. Inspired by the success of dictionary learning based descriptor and classifier, in this paper, we proposed a bottom-up dictionary learning based classification (BUDL-C) for face recognition. In BUDLC, we generate the image descriptor via encoding local patches on a learned dictionary with a regularization of spatial and appearance consistence. Then with the generated image descriptor, a structured discriminative dictionary is learned for the image classifier by using the mixed-norm joint sparse representation. The BUDLC are extensively evaluated on several benchmark face databases, such as AR, Multi-PIE and LFW. Experimental results demonstrate that our algorithm outperforms many existing face recognition approaches.