基于肤色和AdaBoost算法的彩色人脸图像检测

针对肤色检测对复杂背景下的图像误检率高和AdaBoost算法对多姿态、多人脸图像检测效果不理想的问题,将基于肤色的人脸检测与基于AdaBoost算法的人脸检测结合起来,提出一种新的人脸检测方法,即首先利用肤色和形态学操作分割肤色区域,再根据人脸区域的统计特性筛选出人脸候选区域,然后用AdaBoost级联分类器对候选区域扫描,以精确定位人脸.实验表明,该方法同时具有肤色检测正确率高与AdaBoost算法误检率低的优点,可以有效地运用于多姿态、多人脸和复杂背景的情况,具有较好的检测效果.

Color image face detection based on skin color and AdaBoost algorithm

As skin color detection had high false acceptance rate in color image with complex background and AdaBoost algorithm did not work well in multi-pose and multi-face images, this paper proposed a novel face detection method combined skin color detection and AdaBoost algorithm. Skin color segmentation and morphological operators were used to detect skin regions. These regions were filtered in terms of the statistical characteristics of human face to form candidate face regions. Then these regions were scanned by cascade classifier based on AdaBoost for more accurate face detection. The experimental results show that the new approach can detect face with high detection rate and low false acceptance rate and performance better than skin color detection and AdaBoost algorithm. It can be effectively applied to the cases of multi-pose and multi-face images.