Efficient Facial Feature Detection Using Entropy and SVM

Authors:
Qiong Wang;Chunxia Zhao;Jingyu Yang
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China
Venue:
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Year:
2008

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Abstract

In this paper, an efficient algorithm for facial feature detection is presented. Complex regions in a face image, such as the eye, exhibit unpredictable local intensity and hence high entropy. We use this characteristic to obtain eye candidates, and then these candidates are sent to a SVM classifier to get real eyes. According to the geometry relationship of human face, mouth search region is specified by the coordinates of the left eye and the right eye. And then precise mouth detection is done. Experimental results demonstrate the effectiveness of the proposed method.