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

摘要

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.