基于肤色模型与人脸特征的多姿态人脸检测

Multi-pose face detection based on color model and face feature

传统AdaBoost人脸检测算法使用正面人脸训练分类器,不能检测有任意偏转角度的多姿态人脸。本研究利用各种多姿态人脸中人眼结构变化比较小的性质,使用人眼训练AdaBoost算法的分类器,设计出一种快速的复杂场景下的多人多姿态人脸检测算法。先将图像映射到一种色彩空间,运用皮肤颜色分布特性检测皮肤区域;再运用AdaBoost算法从肤色区域检测出人眼区域;最后根据人眼在人脸中的位置计算出人脸的位置。实验结果表明:该算法对多姿态人脸有很好的检测效果。

Traditionally,AdaBoost based face detection algorithms use frontal faces to train classifiers,which cannot detect multi-pose faces with arbitrary rotations.Noticing that the eye structures vary less than face structures in multi-pose faces,eyes were used to train the classifier in AdaBoost algorithm,and present a fast algorithm to detect multi-pose faces in complex scenes.An image is first mapped to a YCrCb color space and the skin region is detected with the skin color distribution.The eye region is then detected from the skin region via the AdaBoost algorithm.The face is located according to the eye positions relative to faces.Experimental results show that this algorithm performs well for the detection of multi-pose faces.