Multi-view Face Recognition with Min-Max Modular SVMs - Fan, Zhi-Gang; Lu, Bao-Liang (Advances in Natural Computation, 2005)
Through task decomposition and module combination, min-max modular support vector machines (M3-SVMs) can be successfully used for difficult pattern classification task. M3-SVMs divide the training data set of the original problem to several sub-sets, and combine them to a series of sub-problems which can be trained more effectively. In this paper, we explore the use of M3-SVMs in multi-view face recognition. Using M3-SVMs, we can decompose the whole complicated problem of multi-view face recognition into several simple sub-problems. The experimental results show that M3-SVMs can be successfully used for multi-view face recognition and make the classification more accurate.