Combining SVM Classifiers for Multiclass Problem: Its Application to Face Recognition

Jaepil Ko, Hyeran Byun

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)531-539
Number of pages9
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2688
Publication statusPublished - 2003 Dec 1

Fingerprint

Multi-class
Face recognition
Face Recognition
Pairwise
Classifiers
Classifier
Decomposition Method
Decomposition
Support vector machines
Rejection
Error Rate
Support Vector Machine
Face
Binary
Class
Output
Experimental Results

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

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