Improving AdaBoost Based Face Detection Using Face-Color Preferable Selective Attention

Authors:
Bumhwi Kim;Sang-Woo Ban;Minho Lee
Affiliations:
School of Electrical Engineering and Computer Science, Kyungpook National Univ., Puk-Gu, Korea 702-701;Dept. of Information and Communication Engineering, Dongguk Univ., Gyeongju, Korea 780-714;School of Electrical Engineering and Computer Science, Kyungpook National Univ., Puk-Gu, Korea 702-701
Venue:
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Year:
2008

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Abstract

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.