基于图像分割的快速人脸定位方法

Fast Face Detection Based on Image Segmentation

针对图像中人脸部分比非人脸部分小得多的情况,设计了分类器专门用于人脸的粗定位。该分类器算法的主要步骤是,先对图像进行预处理,再使用OAC滤波器滤波,最后使用阈值分割法将图像分割为人脸区域、可能人脸区域和非人脸区域3部分。为人脸细定位算法减少了计算量,增加了准确率。对CMU数据库中的图像进行测试,非人脸区域的排除率能达到99%,误检率仅有1.3%。实验结果证明了该算法的有效性和可靠性。

Face detection is the first step of face recognition. This paper discusses the classifier based on the observation that presence probability of ＂face＂ objects in a scene is substantially smaller compared with that of ＂non-face＂ objects. The main processes of the algorithm are： to preprocess the image first and then filter in Fourier transform domain by OAC and finally segment the image into ＂face＂ area, ＂may-be face＂ area and ＂none-face＂ area. The algorithm reduces the computation of the second level classifier and increases the detection rate, The testing of CMU face database show that the algorithm successfully segments the image with a ＂non-face＂ rejecting rate of 99% and a false alarm rate of 1.3 %. The experiment results confirm the algorithms＇ effectiveness and reliability.