Multi-view Face Recognition with Min-Max Modular SVMs

Abstract. Through task decomposition and module combination, minmax 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 multiview 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.

• Type: Conference
• Year: 2005
• Where: ICNC
• Authors: Zhi-Gang Fan, Bao-Liang Lu
• Keywords: Face Recognition, ICNC 2005, Multi-view Face Recognition, Multiview Face Recognition