基于肤色模型和重心模板的人脸检测

为了获得具有较高检测率并保持较低误检率的快速人脸检测,提出了一种基于知识的人脸检测方法．在人脸粗检中用肤色模型分割出输入图像中的人脸可能区域,并对这些区域进行重心模板匹配过滤出非人脸区域．利用人脸的生理分布特征设计出一种动态的人脸三分图分布模型,并以不同条件下的大量人脸图像样本作为统计数据建立了一个人脸规则知识库,用来判定过滤后的区域是否为人脸．实验结果表明,该方法具有很好的鲁棒性,能快速检测出不同光照、不同大小和有一定旋转角度的人脸．

Face Detection Based on Skin-color Model and Gravity-center Template

To accelerate the speed of face detection and get low false alarm rate, an approach of knowledge-based face detection is proposed. It integrates the skin-color model and the gravity-center template. In the process of rough detection, the skin-color model is used to segment the face like regions from any input image. The face-like regions are further checked out by matching the gravity-center template. As the physical structure of human face being taken into careful consideration, a dynamic three subsections distribution model of face, is proposed and used to establish a face knowledge base by analyzing large numbers of face images under different conditions. All the face-like regions are verified if they are genuine human faces based on the knowledge base of faces. The experimental results show that this approach is robust for human face images under complex background, different sizes and certain degree of rotation.