Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014, Article ID 478482, 9 pages
Research Article

A Support Vector Data Description Committee for Face Detection

1Department of Mechanical Engineering, Chung Yuan Christian University, Chungli 320, Taiwan
2Institute of Nuclear Energy Research, Atomic Energy Council, Taoyuan 325, Taiwan
3Department of Electrical Engineering, National Taiwan Ocean University, Keelung 200, Taiwan

Received 4 November 2013; Accepted 24 December 2013; Published 19 February 2014

Academic Editor: Chung-Hao Chen

Copyright © 2014 Yi-Hung Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Face detection is a crucial prestage for face recognition and is often treated as a binary (face and nonface) classification problem. While this strategy is simple to implement, face detection accuracy would drop when nonface training patterns are undersampled. To avoid these problems, we propose in this paper a one-class learning-based face detector called support vector data description (SVDD) committee, which consists of several SVDD members, each of which is trained on a subset of face patterns. Nonfaces are not required in the training of the SVDD committee. Therefore, the face detection accuracy of SVDD committee is independent of the nonface training patterns. Moreover, the proposed SVDD committee is also able to improve generalization ability of the original SVDD when the face data set has a multicluster distribution. Experiments carried out on the extended MIT face data set show that the proposed SVDD committee can achieve better face detection accuracy than the widely used SVM face detector and performs better than other one-class classifiers, including the original SVDD and the kernel principal component analysis (Kernel PCA).