Multi-class Support Vector Machines with Case-Based Combination for Face Recognition

Abstract. The support vector machine is basically to deal with a two-class classification problem. To get M-class classifiers for face recognition, it is common to construct a set of binary classifiers f1 ,…,fM , each trained to separate one class from the rest. The multi-class classification method has a main shortcoming that the binary classifiers used are obtained by training on different binary classification problems, and thus it is unclear whether their real-valued outputs are on comparable scales. In this paper, we try to use additional information, relative outputs of the machines, for final decision. We propose case-based combination with reject option to use the information. The experiments on the ORL face database shows that the proposed method achieves a slight better performance than the previous multi-class support vector machines.

• Type: Conference
• Year: 2003
• Where: CAIP
• Authors: Jaepil Ko, Hyeran Byun
• Keywords: Binary Classifiers, CAIP 2003, Classification Problems, Support Vector Machines