Sequential Fusion of Output Coding Methods and Its Application to Face Recognition

Jaepil KO; Hyeran BYUN

Sequential Fusion of Output Coding Methods and Its Application to Face Recognition

Jaepil KO, Hyeran BYUN

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In face recognition, simple classifiers are frequently used. For a robust system, it is common to construct a multi-class classifier by combining the outputs of several binary classifiers; this is called output coding method. The two basic output coding methods for this purpose are known as OnePerClass (OPC) and PairWise Coupling (PWC). The performance of output coding methods depends on accuracy of base dichotomizers. Support Vector Machine (SVM) is suitable for this purpose. In this paper, we review output coding methods and introduce a new sequential fusion method using SVM as a base classifier based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with others. The experimental results show that our proposed method can improve the performance significantly on the real dataset.

Publication: IEICE TRANSACTIONS on Information Vol.E87-D No.1 pp.121-128

Publication Date: 2004/01/01

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Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)

Category: Face

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Jaepil KO, Hyeran BYUN, "Sequential Fusion of Output Coding Methods and Its Application to Face Recognition" in IEICE TRANSACTIONS on Information, vol. E87-D, no. 1, pp. 121-128, January 2004, doi: .
Abstract: In face recognition, simple classifiers are frequently used. For a robust system, it is common to construct a multi-class classifier by combining the outputs of several binary classifiers; this is called output coding method. The two basic output coding methods for this purpose are known as OnePerClass (OPC) and PairWise Coupling (PWC). The performance of output coding methods depends on accuracy of base dichotomizers. Support Vector Machine (SVM) is suitable for this purpose. In this paper, we review output coding methods and introduce a new sequential fusion method using SVM as a base classifier based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with others. The experimental results show that our proposed method can improve the performance significantly on the real dataset.
URL: https://globals.ieice.org/en_transactions/information/10.1587/e87-d_1_121/_p

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@ARTICLE{e87-d_1_121,
author={Jaepil KO, Hyeran BYUN, },
journal={IEICE TRANSACTIONS on Information},
title={Sequential Fusion of Output Coding Methods and Its Application to Face Recognition},
year={2004},
volume={E87-D},
number={1},
pages={121-128},
abstract={In face recognition, simple classifiers are frequently used. For a robust system, it is common to construct a multi-class classifier by combining the outputs of several binary classifiers; this is called output coding method. The two basic output coding methods for this purpose are known as OnePerClass (OPC) and PairWise Coupling (PWC). The performance of output coding methods depends on accuracy of base dichotomizers. Support Vector Machine (SVM) is suitable for this purpose. In this paper, we review output coding methods and introduce a new sequential fusion method using SVM as a base classifier based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with others. The experimental results show that our proposed method can improve the performance significantly on the real dataset.},
keywords={},
doi={},
ISSN={},
month={January},}

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TY - JOUR
TI - Sequential Fusion of Output Coding Methods and Its Application to Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 121
EP - 128
AU - Jaepil KO
AU - Hyeran BYUN
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E87-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2004
AB - In face recognition, simple classifiers are frequently used. For a robust system, it is common to construct a multi-class classifier by combining the outputs of several binary classifiers; this is called output coding method. The two basic output coding methods for this purpose are known as OnePerClass (OPC) and PairWise Coupling (PWC). The performance of output coding methods depends on accuracy of base dichotomizers. Support Vector Machine (SVM) is suitable for this purpose. In this paper, we review output coding methods and introduce a new sequential fusion method using SVM as a base classifier based on OPC and PWC according to their properties. In the experiments, we compare our proposed method with others. The experimental results show that our proposed method can improve the performance significantly on the real dataset.
ER -