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Published in:
2008 | OriginalPaper | Chapter
Improving AdaBoost Based Face Detection Using Face-Color Preferable Selective Attention
Authors : Bumhwi Kim, Sang-Woo Ban, Minho Lee
Published in: Intelligent Data Engineering and Automated Learning – IDEAL 2008
Publisher: Springer Berlin Heidelberg
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In this paper, we propose a new face detection model, which is developed by combining the conventional AdaBoost algorithm for human face detection with a biologically motivated face-color preferable selective attention. The biologically motivated face-color preferable selective attention model localizes face candidate regions in a natural scene, and then the Adaboost based face detection process only works for those localized face candidate areas to check whether the areas contain a human face. The proposed model not only improves the face detection performance by avoiding miss-localization of faces induced by complex background such as face-like non-face area, but can enhances a face detection speed by reducing region of interests through the face-color preferable selective attention model. The experimental results show that the proposed model shows plausible performance for localizing faces in real time.