Random subspace support vector machine ensemble for reliable face recognition

Bailing Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Face recognition still meets challenges despite the progresses made. One of less addressed problems is to reject unregistered subjects. Aiming to tackle this problem, this paper proposes random subspace support vector machine (SVM) ensemble to provide classification confidence and implement reject option to accommodate the situations where no classification should be made. The ensemble is created using the random subspace (RS) method, together with four feature descriptions including local binary pattern (LBP), pyramid histogram of oriented gradient (PHOG), Gabor filtering and wavelet transform. The consensus degree from the ensemble's voting conforms to the confidence measure and rejection is accomplished accordingly when the confidence falls below a threshold. The reliable recognition scheme is empirically evaluated on several benchmark face databases including AR faces, FERET faces and Yale B faces, all of which yielded highly reliable results, thus demonstrating the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalInternational Journal of Biometrics
Volume6
Issue number1
DOIs
Publication statusPublished - 2014

Keywords

  • Random subspace
  • Reliable face recognition
  • Support vector machine ensemble

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