Multi-subregion face recognition using coarse-to-fine Quad-tree decomposition

One problem of existing appearance-based face recognition methods (e.g. PCA, LDA) is their weak ability of coping with local variations caused by facial expressions, motion deformation, data missing, etc. Multi-subregion fusion methods, which divide the face into a set of subregions, aim at this issue, and were reported a better performance. However, it leaves two open questions: 1. what subregions are good partitions on the face, and 2. How to fuse these subregions could achieve the expected performance. In this paper, we address these two questions and propose a local discrimination driven face partition method based on a coarse-to-fine Quad-tree decomposition. Unlike other multi-subregion approaches relying on prior knowledge, our method partitions the face according to the data property. Thus, it can adapt to varied databases. Meanwhile, our method introduces an optimized solution that fuses selected subregions to reach higher recognition accuracy. The cross-database experiments including one 3D database and three 2D databases demonstrate the efficiency and effectiveness of the proposed method.