摘要
对于常用的基于Haar特征的AdaBoost人脸检测算法存在漏检率与误检率高等不足,增加了Haar特征的扩展种类,这些新增Haar特征能够有效减少因眉毛与眼睛灰度值近似而引起的误判,同时去除一些针对人脸分辨效果不好的特征来提高算法的实时性,深入分析了利用Haar特征与AdaBoost算法构成的级联分类器的特点.实验数据结果验证了改进后算法的可行性.
Aiming at the high undetected rate and false detection rate, and other less which are existed in the Ada Boost algorithm based on Haar feature for face detection, the expanded categories of Haar features are added in this paper, and it can effectively reduce the erroneous judgement caused by the approximation of the gray value between the eyebrows and eyes. At the same time, the real-time of algorithm is improved by removing some features having bad effect for face detection. The cascade classifier constituting of Haar feature and AdaBoost algorithm is analyzed in depth. Finally, the experimental results verify the feasibility of the improved algorithm.
出处
《计算机系统应用》
2015年第9期152-155,共4页
Computer Systems & Applications
