Computer Engineering and Applications ›› 2007, Vol. 43 ›› Issue (17): 241-244.

• 工程与应用 • Previous Articles     Next Articles

Face detection based on fuzzy set theoretics

ZHANG Jian1,2,TAN Guo-xin2,SONG Wan-juan1   

  1. 1.Department of Computer Science,Huazhong Normal University,Wuhan 430079,China
    2.Engineering Research Center for Education Information Technology,Huazhong Normal University,Wuhan 430079,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-06-11 Published:2007-06-11
  • Contact: ZHANG Jian

基于模糊集理论的人脸检测

张 剑1,2,谈国新2,宋婉娟1   

  1. 1.华中师范大学 计算机科学系,武汉 430079
    2.华中师范大学 教育信息技术工程研究中心,武汉 430079
  • 通讯作者: 张 剑

Abstract: This paper analyses the feasibility that fuzzy set theoretics apply to face detection.It trains the sample set by Haar-like feature and membership function,and selects appropriate weak classifiers through the entropy of feature set and AdaBoost learning algorithm.Then distribution face detector is established.In the detection process,detector can rapidly wash out the sub-window which is unlike face through the front of simple stronger classifiers;for the sub-window which is like face,in terms of similar degree distributer can dynamically select the back of stronger classifiers to determine whether it is a face.This face detector has been tested on the MIT+CMU frontal face test set,the results show that it can effectually improve detection efficiency in the condition of detection performance reducing not too much.

摘要: 分析了模糊集理论运用于人脸检测的可行性,采用Haar矩形特征和隶属度函数对样本集进行训练,运用特征集的熵和AdaBoost算法选取适当的弱分类器,并构建了分发型人脸检测器。检测时,对于不像人脸的子窗口通过靠前的结构简单的强分类器快速将其淘汰掉;对于像人脸的子窗口,根据其与人脸的相似程度,由分发器动态地选择后面的强分类器进行判定。在MIT+CMU的正面人脸图片集中进行了测试,实验结果表明,此检测器在检测性能降低不大的情况下,可以有效地提高检测效率。