期刊文献+

基于矩阵行矢量鉴别矢量集的人脸识别 被引量:2

Face recognition based on discriminant vectors derived from row vectors of matrix
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摘要 提出了一种基于矩阵行矢量鉴别矢量集的人脸识别方法。考虑到人脸图像的行矢量是人脸图像的子模式,可以分别基于这些行矢量求取鉴别矢量集,并用人脸图像在该鉴别矢量集上的投影作为描述人脸的特征。实验结果表明,提出的方法要优于文献[4]的方法。 In this paper,we propose a new face recognition method based on discriminant vectors derived from row vectors of a matrix.Considering the row vectors of face image are sub-patterns of the face image,we can resolve discriminant vectors based those row vectors,then using the projection of face image on discriminant vectors as the features presenting face image.The experiment results show that the method in this paper is superior to the method in reference[4].
出处 《计算机工程与应用》 CSCD 北大核心 2007年第18期205-206,210,共3页 Computer Engineering and Applications
关键词 行向量 子模式 鉴别矢量集 row vectors sub-pattern discriminant vectors
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参考文献4

  • 1Turk M,Pentland A.Face recognition using eigenfaces[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,1991:586-591. 被引量:1
  • 2Belhumeur P N,Hespanha J P,Kriengman D J.Eigenfaces vs.fisherfaces:recognition using class specific linear projection[J].IEEE Trans Pattern Anal Mach Intell,1997,19(7):711-720. 被引量:1
  • 3Liu K,Cheng Y Q,Yang J Y.Algebraic feature extraction for image recognition based on an optimal discriminant criterion[J].Pattern Recognition,1993,26(6):903-911. 被引量:1
  • 4Yang J,Yang J Y.From image vector to matirx:a straightforward image projection technique-IMPCA vs.PCA[J].Pattern Recongition,2002,35:1997-1999. 被引量:1

同被引文献16

  • 1高湘萍,许丹,吴小培.基于核Fisher判别分析的意识任务识别新方法[J].计算机技术与发展,2006,16(9):82-84. 被引量:6
  • 2Burge M, Burger W. Ear Biometrics [M]. Boston: KluwerAcademic Publishers, 1999: 273-286. 被引量:1
  • 3Fukunaga K. Introduction to Statistical Pattern Recognition: 2nd ed [M]. New York: Academic Press, Inc, 1990: 94-102. 被引量:1
  • 4JIANG Wei, TAO Jun-wei, WANG Li-li. A Novel Palmprint Recognition Algorithm Based on PCA&FLD [C]// International Conference on Digital Teleeommunications, Cote d'Azur, Aug 29-31, 2006. Washington D C, USA: IEEE, 2006: 28-31. 被引量:1
  • 5Xiong Huilin, Swamy M N S. Ahmad M O. Two-dimensional FLD for Face Recognition [J]. Pattern Recognition (S0031-3203), 2005, 38(7): 1121-1124. 被引量:1
  • 6Yang J, Zhang D, Frangi A F, et al. Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recogntion [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence(S0162-8828), 2004, 26(1): 131-137. 被引量:1
  • 7Yu H, Yang J. A direct LDA algorithm for high dimensional data-with application to face recognition [J]. Pattern Recognition(S0031-3203), 2001, 34(10): 2067-2070. 被引量:1
  • 8李道红,陈松灿.线性判别分析新方法研究及其应用[D].南京:南京航空航天大学,2003:18-21. 被引量:1
  • 9Duda Richard O,Hart Peter E,Stork David G.模式分类[M].北京:中信出版社&机械工业出版社,2006:24-28. 被引量:1
  • 10FUKUNAGA K. Introduction to statistical pattern recognition [ M ]. 2nd ed. New York : Academic Press, 1990:94-102. 被引量:1

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