Title: Random subspace support vector machine ensemble for reliable face recognition

Authors: Bailing Zhang

Addresses: Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China

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.

Keywords: reliable face recognition; random subspace; support vector machines; SVM ensemble; reliability; biometrics; local binary pattern; LBP; pyramid histogram of oriented gradient; PHOG; Gabor filtering; wavelet transform.

DOI: 10.1504/IJBM.2014.059636

International Journal of Biometrics, 2014 Vol.6 No.1, pp.1 - 17

Received: 07 Jun 2013
Accepted: 20 Sep 2013

Published online: 04 Mar 2014 *

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