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

Jaepil Ko, Hyeran Byun

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)121-128
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE87-D
Issue number1
Publication statusPublished - 2004 Jan 1

Fingerprint

Face recognition
Classifiers
Fusion reactions
Support vector machines
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Cite this

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Sequential Fusion of Output Coding Methods and its Application to Face Recognition. / Ko, Jaepil; Byun, Hyeran.

In: IEICE Transactions on Information and Systems, Vol. E87-D, No. 1, 01.01.2004, p. 121-128.

Research output: Contribution to journalArticle

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