摘要
为解决当被检测图像中具有复杂背景或者含有多人脸时,不能够快速准确的进行人脸检测的问题,本文提出一种基于肤色分割和改进AdaBoost算法的人脸检测方法。首先利用肤色分割方法对样本图像实现图像的预处理,排除样本图像的复杂背景和人体非肤色区域,简化后续的人脸检测工作。然后对AdaBoost算法的弱分类器使用双阈值判决方法,以减少弱分类器个数,提升训练速度;改进权值更新规则,防止训练过程中出现过分配现象。最后对基于肤色分割得到的区域图像利用改进后的Adaboost算法进行最后的精确人脸检测。仿真试验表明,两种算法结合后在训练速度上提升,在检测速度和检测率上有明显提高。
To solve the problem that can not detect face quickly and accurately when the detected images has complex background or contain more faces,A face detection algorithm combined with skin color segmentation and improved Adaboost algorithm is presented.Firstly,pretreatment of sample images to achieve image by using skin color segmentation,exclusion of sample images with complex background and human non-skin regions,the follow-up work has been simplified.Then the dual-threshold decision method of weak classifiers Adaboost algorithm is used to reduce the number of weak classifiers and improve the training speed.Improved weight update rules to prevent distribution of the phenomenon appeared in the training process. Finally, the improved Adaboost algorithm is used to get the final accurate face detection based on areas of the image through skin color segmentation. Simulation results show that the combination of two algorithms improved the training speed, the detection speed and detection rate has improved significantly.
出处
《电子设计工程》
2014年第12期167-170,共4页
Electronic Design Engineering
基金
国家自然基金(61374127)
黑龙江省青年基金(QC2013C066)
黑龙江省博士后科研启动资金(LBH-Q12143)
关键词
肤色分割
双阈值
权值更新
预处理
过分配
人脸检测
shin color
segmentation
dual-threshold
weight update
pretreatment
distribution
face detection
作者简介
段玉波（1951-），男，黑龙江木兰人，博士，教授。研究方向：智能控制、最优滤波、混沌识别、神经网络理论及应用。
