Face detection in pattern recognition and machine vision technology as a long-term subject of concern, has high academic value and commercial value.With the rapid development of intelligent computing technology, new methods and new technologies will continue to be used, which bring more vitality to the research, and detection result will be increasingly better and faster.Firstly, this paper introduces the face detection's background,summarizes and analyzes current algorithms on face detection. The research on the color-based face detection technology is present.First.Gaussian model in the YCbCr chrominance space is built to segment the non-skin color pixels from the image.Then,the characteristics of the possible face blocks are studied,so we can extract candidate human face regions.This process lays a solid foundation the following detection step.The experiments show that this system obtains good detection performance in complex background, with high robustness for illumination,expression and other changes.Secondly, using face detection method based on AdaBoost learning algorithm,which fast calculate haar-like features by"integral figure"method, to build a robust cascade classifier.Focusing on the disadvantages of classical AdaBoost algorithm,this paper analyses the issues of degeneration and distortion of sample weights in training process and come up with a new method to avoid the phenomenon of degeneration in a certain extent.The experimental results show that the new method effect is remarkable.Finally, as skin color detection achieves high false acceptance rate in color image with complex background and AdaBoost algorithm does not work well in multi-pose and multi-face images,a novel face detection method combined skin color detection and AdaBoost algorithm is proposed in this paper.The experimental results show that the new scheme is able to detect faces 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. |