Multi-view Face Recognition with Min-Max Modular SVMs
نویسندگان
چکیده
Through task decomposition and module combination, minmax modular support vector machines (M-SVMs) can be successfully used for difficult pattern classification task. M-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 M-SVMs in multi-view face recognition. Using M-SVMs, we can decompose the whole complicated problem of multiview face recognition into several simple sub-problems. The experimental results show that M-SVMs can be successfully used for multi-view face recognition and make the classification more accurate.
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Multi-View Face Recognition with Min-Max Modular Support Vector Machines
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