Face detection in complex background based on skin color features and improved AdaBoost algorithms

Face detection is a very hot topic in research application of pattern recognition and computer vision. It is widely applied in artificial intelligence, video surveillance, identity authentication, human-machine interaction and so on. However, skin color detection has high false positive rate in complex background and AdaBoost algorithm was not satisfactory for detection of multi-pose and multi-face image. So a novel face detection method combined with skin color detection and an improved AdaBoost algorithm is proposed in this paper. First, it applies skin model segmentation and morphological operators to detect skin regions in the image. And according to the geometrical characteristics of the face, it screens the candidate face regions. Then by the improved classifiers in a cascade structure based on AdaBoost, it achieves more accurate promising regions of face. The experiment results show that this face detection algorithm improves the detection speed in both the quality of detection, and it can effectively reduce the error detection rate of single test method. This method has a good performance on image with complex background. Above all, this method has a certain theory value and practical value.