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IEICE Transactions on Information and Systems 2005 E88-D(10):2373-2379; doi:10.1093/ietisy/e88-d.10.2373
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Copyright © 2005 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Papers -- Image Recognition, Computer Vision

Composite Support Vector Machines with Extended Discriminative Features for Accurate Face Detection

Tae-Kyun KIM1,3 and Josef KITTLER2

1 The author is with Samsung Advanced Institute of Technology, Korea. E-mail: tkk22{at}cam.ac.uk, 2 The author is with Centre for Vision, Speech and Signal Processing, University of Surrey, U.K., 3 Presently, with Machine Intelligence Laboratory, University of Cambridge, U.K.

This paper describes a pattern classifier for detecting frontal-view faces via learning a decision boundary. The proposed classifier consists of two major parts for improving classification accuracy: the implicit modeling of both the face and the near-face classes resulting in an extended discriminative feature set, and the subsequent composite Support Vector Machines (SVMs) for speeding up the classification. For the extended discriminative feature set, Principal Component Analysis (PCA) or Independent Component Analysis (ICA) is performed for the face and near-face classes separately. The projections and distances to the two different subspaces are complementary, which significantly enhances classification accuracy of SVM. Multiple nonlinear SVMs are trained for the local facial feature spaces considering the general multi-modal characteristic of the face space. Each component SVM has a simpler boundary than that of a single SVM for the whole face space. The most appropriate component SVM is selected by a gating mechanism based on clustering. The classification by utilizing one of the multiple SVMs guarantees good generalization performance and speeds up face detection. The proposed classifier is finally implemented to work in real-time by cascading a boosting based face detector.

Key Words: pattern classification, face detection, support vector machine, independent component analysis, principal component analysis, Adaboost

Manuscript received July 30, 2004. Manuscript revised March 17, 2005.


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