期刊文献+

基于灰度信息的人脸检测算法 被引量:4

Face Detection System Based on Gray Information
在线阅读 下载PDF
导出
摘要 提出了一种基于人眼灰度信息的人脸识别算法。该算法首先根据眼球区域的灰度比它周围区域的灰度低,初步地筛选出可能含有人脸的图像,再把筛选出的图像分割成小图像块,根据图像块的复杂度对人两只眼睛的大概位置进行定位,最后采用居中法,进一步确定人眼的存在,进而确定人脸的确切位置。实验结果表明:此算法具有较高的检测正确率及较快的检测速度。 This article advances a calculation method about face location. Face location is an important basis for automatic human face recognition. In this paper, we have two steps to detect human face. The first step is to find the picture which has the likelihood to have human face by using gray character information. The second step is to use a method to look for the center of eyes. According to experiments, it is concluded that the calculation method has a great efficiency and speed.
出处 《苏州科技学院学报(工程技术版)》 CAS 2005年第2期81-83,共3页 Journal of Suzhou University of Science and Technology (Engineering and Technology)
基金 湖北省自然科学基金项目(No2004ABA068)
关键词 人脸检测 灰度 居中法 automatic human face detection gray information center
  • 相关文献

参考文献4

  • 1Yang Ming-Hsuan, Kriegman David J, Ahuja Narendra. Detecting faces in images:A survey[J]. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2002,24(1):34-58.
  • 2Sung K-K, Poggio T. Example-based learning forview-based human face detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998,20(1 ) :39-51.
  • 3Dario Maio, Davide Maltoni. Real-time face location on gray-scale static images[J]. Pattern Recognition, 2000,33: 1525-1539.
  • 4H Wu, Q Chen, M Yachida. Face detection from color image using a fuzzy pattern matching method[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999,21 ( 6 ): 557-563.

同被引文献14

  • 1王昱,阎苹,丁明跃.一个快速人脸识别系统[J].计算机与数字工程,2004,32(3):16-18. 被引量:3
  • 2赵丽红,孙晓琳,王宇飞,徐心和.基于肤色的人脸检测[J].工程图学学报,2005,26(3):84-88. 被引量:9
  • 3张杰,杨晓飞,赵瑞莲.基于Hough变换圆检测的人眼精确定位方法[J].计算机工程与应用,2005,41(27):43-44. 被引量:36
  • 4Wen G, Bo C, Shah Shi-guang, et al.The CAS-PEAL large-scale Chinese face database and baseline evaluations[J].IEEE Transac- tions on Systems, Man and Cybernetics,part A: Systems and Hu- mans,2008,38( 1 ) : 149-161.
  • 5Gao Y,Leung M K H.Face recognition using line edge map[J]. IEEE Transactions on Pattern Analysis and Machine Intelli-gence, 2002,24: 764-779.
  • 6Hanouz M, Kit'tler J, Kamarainen J K, et al.Feature-based af- fine-invariant localization of faces[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27: 1490-1495.
  • 7Viola P, Jones M.Robust real-time object detection, TR CRL 2001/01[R].Cambridge, UK: Cambridge Research Laboratory, 2001.
  • 8Freund Y, Schapire R E.A decision-theoretic generalization of online learning and an application to boosting[J].Journal of Computer and System Sciences, 1997 ( 1 ) : 55-119.
  • 9Lienhart R, Kuranov A, Pisarcvsky V.Empirical analysis of detection cascades of boosted classifiers for rapid object detection[C]// Proceedings of the 25th German Pattern Recognition Sympo- sium, Magdeburg, 2003 : 297-304.
  • 10C.H.Lee, J.S.Kim, K.H.Park. Automatic human face location in a complex background using motion and color information, Pattern Recognition[J]. 1996, 29 ( 11 ) : 1877-1899.

引证文献4

二级引证文献19

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部