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.
|Number of pages||7|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2003 Dec 1|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)