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...

• Type: Conference
• Year: 2002
• Where: ICIP
• Authors: Tae-Kyun Kim, Donggeon Kong, Sang Ryong Kim
• Keywords: Decision Boundary Estimation, ICIP 2002, Image Processing, Pattern Classification Approach, Subsequent Classification Stage