基于扩展三角特征的AdaBoost快速人眼检测算法  

AdaBoost fast eye detection algorithm based on extended triangular features

作　　者：黄坤[1] 韩飞[1] 杨月全[1] 王正群[1] 张天平[1] 

机构地区：[1]扬州大学信息工程学院,扬州225127

出　　处：《北京科技大学学报》2012年第1期48-52,共5页Journal of University of Science and Technology Beijing

基　　金：国家自然科学基金资助项目(60874045);江苏省高校自然科学研究资助项目(10KJB510027);江苏省自然科学基金资助项目(BK2009184)

摘　　要：首先给出了通过矩形块与三角像素特征块相结合所构造的八种用于眼睛检测的扩展三角特征原型块.考虑扫描块在人脸背景中遍历时眼睛样本图像块数量远少于非眼睛样本块数的实际,提出了一种结合Haar特征和三角特征的AdaBoost快速眼睛检测算法.通过级联分类器的前几层强分类器完成排除大部分非眼睛样本;然后,通过后续强分类器进行判断大部分的眼睛图像块和少量非眼睛图像块.检测时间消耗有所下降,这样可以保证整体的检测速度.实验结果进一步表明该算法具有更好的检测性能,与仅使用Haar特征相比正检率有一定程度提高.Eight extended feature prototypes were presented by combining rectangular feature blocks and triangular feature blocks. In consideration of the fact that the amount of eye image blocks is far less than that of non-eye image blocks during a scanning block passing through face images, a fast eye location detection scheme based on AdaBoost algorithm combining rectangular feature blocks and triangular feature blocks was proposed. After most of non-eye blocks are excluded through the foregoing strong classifiers, most eye image blocks and a few of non-eye image blocks are detected through the rear parts of the cascade classifier, which can reduce the detection time and boost the detection speed. The experiments further show that the scheme has better detection performance and positive detection rate compared to the case only employed Haar features.

关 键 词：人眼检测 算法 面部特征 特征抽取 模式分类 图像匹配 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]