【摘要】 Adaboost算法是一种被广泛应用于人脸检测的分类器学习方法,通过Haar-like特征和样本的学习和训练,形成一个强分类器,能有效地区分人脸跟非人脸。文中提出一种Adaboost结合最小割算法的人脸提取方法,该方法着眼于图像中的轮廓及肤色信息,对每个点设置一个权值,寻找一条权值最小的边界,准确提取出人脸。实验结果表明,Adaboost和最小割的人脸提取算法,分割效果较好,且耗时较小。更多还原

【Abstract】 Adaboost algorithm is widely used in face detection.It’s a face classify learning algorithm method.Through the learning over Haar-like features and samples,it develops a strong classifier that can divide face and non-face region.Arrange the strong classify in some order,which forms a more efficient face classify.In this paper,we propose an algorithm based on min-cut and Adaboost algorithm in order to detect face better.This algorithm focuses on the outline and the skin-color pixel in the image,and finds the boundary with least total cost.The experiment result showed this algorithm can achieve face division efficiently.更多还原