Combining SVM classifiers for multiclass problem: its application to face recognition

Authors:
Jaepil Ko;Hyeran Byun
Affiliations:
Dept. of Computer Science, Yonsei Univ., Seoul, Korea;Dept. of Computer Science, Yonsei Univ., Seoul, Korea
Venue:
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Year:
2003

Citing 7
Cited 1

Classification by pairwise coupling

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Support vector machines applied to face recognition

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Improved Pairwise Coupling Classification with Correcting Classifiers

ECML '98 Proceedings of the 10th European Conference on Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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Abstract

In face recognition, a simple classifier such as k-NN is frequently used. For a robust system, it is common to construct the multi-class classifier by combining the outputs of several binary ones. The two basic schemes for this purpose are known as one-per-class (OPC) and pairwise coupling (PWC). The performance of decomposition methods depends on accuracy of base dichotomizers. Support vector machine is suitable for this purpose. In this paper, we give the strength and weakness of two representative decomposition methods, OPC and PWC. We also introduce a new method combining OPC and PWC with rejection based on the analysis of OPC and PWC using SVM as base classifiers. The experimental results on the ORL face database show that our proposed method can reduce the error rate on the real dataset.