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As a result of statistical learning theory, support vector machines (SVMs)(23) are effective classifiers for the classification problems. SVMs have been successfully applied to various pattern classification problems, such as handwritten digit recognition, text categorization and face detection, due to their powerful learning ability and good generalization ability. However, SVMs require to solve a quadratic optimization problem and need training time that are at least quadratic to the number of training samples. Therefore, many large-scale problems by using traditional SVMs are too hard to be solved. To overcome this difficulty, Lu and colleagues have proposed a min-max modular support vector machine (M3-SVM) and part-versus-part task decomposition method (16). A very important advantage of M3- SVMs over traditional SVMs is that a two-class problem can be further decomposed into a series of two-class subproblems. The M3-network model (15) has been applied successfully to many real-world applications such as part-of-speech tagging (17), single-trial EEG signal classification (18), prediction of protein subcellular multi-locations (26), face recognition (2, 13) and text categorization (14). The basic idea behind M3-network is the "divide and conquer" strategy. The task decomposition scheme of M3-network is based on class relations, and the instances in the same class can be further decomposed randomly (15), according to parallel hyperplanes (24), or prior knowledge (13). The learning procedure of each subproblems is independent, and therefore parallel learning can be implemented easily. The combination strategy follows two principles, the minimization principle and the maximization principle (15). We explore the use of M3-SVMs in multi-view face recognition. Multi-view face recognition is a more challenging task than frontal view face recognition. Face recognition techniques have been developed over the past few decades. But many of those existing face recognition techniques, such as Eigenfaces and Fisher-faces (22, 1), are only effective for frontal view faces. The difficulties of multi-view face recognition is obvious because of the complicated nonlinear manifolds existing in the data space. Using M3-SVMs, we can decompose the

Multi-View Face Recognition with Min-Max Modular Support Vector Machines