Abstract
In face recognition, a simple classifier such as k -NN is frequently used. For a robust system, it is common to construct the multiclass 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.
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Ko, J., Byun, H. (2003). Combining SVM Classifiers for Multiclass Problem: Its Application to Face Recognition. In: Kittler, J., Nixon, M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003. Lecture Notes in Computer Science, vol 2688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44887-X_63
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DOI: https://doi.org/10.1007/3-540-44887-X_63
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