期刊文献+

脸部特征点定位方法综述 被引量:8

Review to facial features localization approaches
在线阅读 下载PDF
导出
摘要 人脸识别技术极大推动了图像处理、模式识别、计算机视觉等诸多学科的发展。人脸部特征点的定位是人脸识别中的关键步骤,定位准确与否直接关系到后续应用的可靠性。系统综述了特征点定位六大类方法,分为基于灰度信息、先验规则、几何形状、统计模型、小波和3D方法,并给出了对各方法的性能评价以及对未来的展望。 Face recognition technology has greatly promoted the development of image processing, pattern recognition and computer vision. Human facial features positioning is a key stage in face recognition and the accuracy of the positioning directly relates to the reliability of subsequent applications. This paper systematically reviews the six categories of methods in facial features positioning, namely, those based on grey-level information, a priori knowledge, geometric shapes, statistic models, wavelet and three dimensions. It evaluates the above-mentioned methods and expresses prospects.
出处 《计算机工程与应用》 CSCD 2012年第1期180-182,218,共4页 Computer Engineering and Applications
基金 2011年安徽高校省级自然科学研究项目重点项目(No.KJ2011A040)
关键词 人脸识别 人脸特征点定位 脸部特征提取 face recognition facial features positioning facial features extraction
  • 相关文献

参考文献7

  • 1Kanade T.Computer recognition of human faces[D].Japan:Kyoto University, 1974.
  • 2Wiskott L,Fellous J M,Kruger N,et al.Face recognition by elastic bunch graph matching[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997,19(7) :775-779.
  • 3林维训,潘纲,吴朝晖,潘云鹤.脸部特征定位方法[J].中国图象图形学报(A辑),2003,8(8):849-859. 被引量:42
  • 4崔国勤,李锦涛,高文,焦锋.基于支持向量机的人脸识别方法[J].计算机科学,2003,30(4):11-15. 被引量:13
  • 5Brunelli R,Poggio T.Face recognition: features versus templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993,15(10) : 1042-1052.
  • 6柴秀娟,山世光,卿来云,陈熙霖,高文.基于3D人脸重建的光照、姿态不变人脸识别[J].软件学报,2006,17(3):525-534. 被引量:54
  • 7阮秋琦.数字图像处理学[M].北京:电子工业出版社,2004.

二级参考文献31

  • 1柴秀娟,山世光,高文,陈熙霖.基于样例学习的面部特征自动标定算法[J].软件学报,2005,16(5):718-726. 被引量:15
  • 2容观澳.计算机图像处理[M].清华大学出版社,2000.269-288.
  • 3Vapnik V. The nature of statistical learning theory. Springer,New York, 1995.
  • 4Turk M, Pentland A. EigenIaces for recognition. J. Cognitive Neuroscience, 1991,3(1) ; 71~86.
  • 5Kirby M,Sirovich L.Application of the Karhunen-Loève Procedure fot the characterization of human faces.IEEE Trans.on Pattern Analysis and Machine Intelligence,1990,12(1):103~108 Ries F,Nagy B S Z.Function Analysis(Vol 2)泛函 分析讲义(第二卷).科学出版社,1980.110~116.
  • 6Collobert R,Bengio S. Support Vector Machines for Large-Scale Regression Problems ,IDIAP-RR-00-17,2000.
  • 7Snika A,Scholkopf V. A tutorial on support vector regression.NeuroColt 2 : [TR 1998-03]. 1998.
  • 8Platt J C. Fast training of support vector machines using sequential minimal optimization. Advances in Kernel Methods,MIT press, 1999. 271~284.
  • 9Shevade S K, et al. Improvements to SMO Algorithm for SVM Regression.- [Technical Report CD-99-16].
  • 10Weston J, Watkins C. Multi-class Support Vector Machines:[Techical Report CSD-TR-9804].

共引文献122

同被引文献43

引证文献8

二级引证文献48

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