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Learning a decision boundary for face detection

11 years 1 months ago
Learning a decision boundary for face detection
This paper describes a pattern classification approach for detecting frontal-view faces via learning a decision boundary. The classification can be achieved either by explicit estimation of density functions of two classes, face and non-face or by direct learning of a classification function (decision boundary). The latter is a more effective approach, when the number of training available examples is small, compared to the dimensionality of image space. The proposed method consists of a implicit modeling of both face and near-face classes using Independent Component Analysis (ICA), and a subsequent classification stage based on the decision boundary estimation using Support Vector Machine (SVM). Multiple nonlinear SVMs are trained for local subspaces, considering the general non-Gaussian and multi-modal characteristic of face space. This parallelization of SVMs reduces computational cost of on-line classification, since the locally trained SVM has small number of support vectors comp...
Tae-Kyun Kim, Donggeon Kong, Sang Ryong Kim
Added 24 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2002
Where ICIP
Authors Tae-Kyun Kim, Donggeon Kong, Sang Ryong Kim
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