【摘要】 在Haar-like特征的基础上增加新的检测特征,给出特征计算方法和积分方法,实现多角度人脸检测。将多角度人脸分为3类,即全侧脸、半侧脸和正面人脸。利用连续Adaboost算法训练各类人脸检测器,用金字塔式结构将各类人脸检测器级联成一个多角度人脸检测器。在CMU人脸检测集合上,该检测器的成功率为85.2%,高于Adaboost算法和浮点Adaboost算法。更多还原

【Abstract】 This paper adds some new detection features on the basis of Haar-like feature,gives the feature calculation method and integration method,and achieves multi-angle face detection.It divides multi-angle face into three categories: all side face,half side face and positive face.Continuous Adaboost algorithm is used to train various types of face detector.It cascades various types of face detectors into a multi-angle face detector by using pyramid-style structure.In CMU face detection aggregation,the success rate of this detector is 85.2% which is higher than that of Adaboost algorithm and float Adaboost algorithm.更多还原