期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

核典型相关性鉴别分析 被引量：1

Kernel discriminant analysis based on canonical correlation
原文传递
导出
摘要 提出一种新的基于典型相关性的核鉴别分析,以图片集为基础的人脸识别算法。把每个图片集映射到一个高维特征空间,然后通过核线性鉴别分析(KLDA)处理,得到相应的核子空间。通过计算两典型向量的典型差来估计两个子空间的相似度。根据核Fisher准则,基于类间典型差与类内典型差的比率建立核子空间的相关性来得到核典型相关性鉴别分析(KDCC)算法。在ORL、NUST603、FERNT和XM2VTS人脸库上的实验结果表明,该算法能够更有效提取样本特征,在识别率上要优于典型相关性鉴别分析(DCC)和核鉴别转换(KDT)算法。 In this study, we propose a new kernel discriminant for learning and recognition of image sets using canonical correlation. Each image set is mapped into a high-dimensional feature space. The corresponding kernel space is then con- structed by a kernel linear discriminant analysis. The similarity of two kernel subspaces is assessed by calculating the ca- nonical difference between them. According to the kernel Fisher discriminant, a Kernel Discriminant Analysis of Canonical Correlation algorithm is derived to establish the correlation between the kernel subspaces based on the ratio of the canonical differences of the between-classes to those of the within-classes. The experimental results on the ORL, NUST603, FERNT and XM2VTS database demonstrate that the proposed method can efficiently extract the features of the images. Moreover, the recognition rate of the proposed algorithm outperforms DCC and KDT.
作者 陈伟琦 程强
机构地区 江南大学数字媒体学院
出处 《中国图象图形学报》 CSCD 北大核心 2012年第12期1516-1521,共6页 Journal of Image and Graphics
基金 江苏省研究生教育创新工程项目(CXLX11_04910) 中央高校基本科研业务费专项资金资助项目(JUSRP211A70)
关键词 典型相关性 典型差 核线性鉴别分析 核鉴别转换 人脸识别 canonical correlation canonical difference kernel linear diseriminant analysis kernel discriminant transfor-mation face recognition
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献16

  • 1Choi S I, Kim C, Choi C H. Shadow compensation in 20 images for face recognition [J]. Pattern Recognition, 2007, 40 ( 7 ) : 2118-2125.
  • 2Lee S W, Moon S H, Lee S W. Face recognition under arbitrary illumina-tion using illuminated exemplars [J]. Pattern Recogni?tion,2oo7, 40(5) :1605-1620.
  • 3Wang M, Hua X S, Hong R C, et al. Unified video annotation via multi -graph learning [J]. IEEE Trans. On Circuit System and Video Technology. 19(5), 2009 :733-746.
  • 4Wang M, Hua X S, TangJ H, et al. Beyond distance measure?ment: constructing neighborhood similarity for video annotation [J]. IEEE trans. On Multimedia. 2009,11 (3) :465476.
  • 5Kim T K, Arandjelovic 0, Cipolla R. Boosted manifold principal angles for image set-based recognition [J] . Pattern Recognition, 2007,40(9): 2475-2484.
  • 6Chu W S, Huang C R, Chen C S. Identifying gender from una?ligned facial images by set classification [CJ II Proceedings of the 2010 20th International Conference on Pattern Recognition. Washington DC, USA: IEEE Computer Society, 2010 : 2636-2639.
  • 7Yamaguchi 0, Fukui K, Maeda K. Face recognition using tem?poral image sequence [CJ IIProceedings of International Confer?ence on Face & Gesture Recognition. Washington DC, USA: IEEE Computer Society, 1998 :318-323.
  • 8Fukui K, Yamaguchi o. Face recognition using multi-view point patterns for robot vision [J]. International Symposium of Robotics Research,2003,11: 192-201.
  • 9Kim T K, KittlerJ, Cipolla R. Discriminative learning and re?cognition of image set classes using Canonical Correlations [J] . IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29 (6) : 1005-1018.
  • 10Fukui K, Stenger B , Yamaguchi O. A framework for 3D object recognition using the kernel constrained mutual subspace method [J]. Lecture Notes in Computer Science,2oo6(3852):315- 324.

二级参考文献23

  • 1Vapnik V. The Nature of Statistical Learning Theory [M].Springer, N Y, 1995
  • 2Scholkopf B, Smola A, Muller K R. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural Computation, 1998,10(5) : 1299- 1319
  • 3Bach F R, Kernel M I. Independent Component Analysis [J].Journal of Machine Learning Research, 2002,3:1 - 48
  • 4Mika S, Ratsch G, Weston J, et al. Fisher discriminant analysis with kernels [A]. In: IEEE International Workshop on Neural Networks for Signal Processing IX, Madison (USA), August,1999. 41-48
  • 5Mika S,Ratsch G,Scholkopf B,et al. Invariant feature extraction and classification in kernel spaces [A]. Advances in Neural Information Processing Systems 12. Cambridge: MIT Press, 1999
  • 6Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach [J]. Neural Computation, 2000,12 (10) : 2385-2404
  • 7Muller K R,Mika S, Ratsch G, et al. An introduction to kerne-based learning algorithms [J]. IEEE Transactions on Neural Networks, 2001,12(2) :181-201
  • 8Yang J,Jin Z,Yang J Y,et al. Essence of kernel Fisher discriminant: KPCA plus LDA [J]. Pattern Recognition, 2004,37:2097-2100
  • 9Yang J, Frangi A F,Yang J Y,et al. KPCA plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature extraction and Recognition [J]. IEEE Trans Pattern Analysis and Machine Intelligence, 2005,27 (2) : 230- 244
  • 10Zadeh L A. Fuzzy sets [J]. Info Control, 1965,8:338-353

共引文献5

同被引文献16

  • 1张鑫.基于SIFT算法的ATM视频人脸识别系统研究.哈尔滨:哈尔滨工程大学,2012.
  • 2Poh N,Chan C H,Kittler J,et al.An evaluation of video-to-video face verification.Information Forensics and Security,IEEE Transactions on,2010; 5(4):781-801.
  • 3Wolf L,Hassner T,Maoz I.Face recognition in unconstrained videos with matched background similarity.Computer Vision and Pattern Recognition (CVPR),2011 IEEE Conference on,IEEE,2011:529-534.
  • 4See J,Eswaran C,Fauzi M F A.Dual-feature bayesian MAP classification:exploiting temporal information for video-based face recognition.Neural Information Processing,Springer Berlin Heidelberg,2012:549-556.
  • 5Bhattacharjee D,Basu D K,Nasipuri M,et al.Human face recognition using fuzzy multilayerperceptron.Soft Computing,2010; 14(6):559-570.
  • 6See J,Eswaran C.Exemplar extraction using spatio-temporal hierarchical agglomerative clustering for face recognition in video.Computer Vision (ICCV),2011 IEEE International Conference on,IEEE,2011:1481-1486.
  • 7Liu X,Tosun D,Weiner M W,et al.Locally linear embedding (LLE) for MRI based Alzheimer's disease classification.NeuroImage,2013; 83(3):148-157.
  • 8See J,Fauzi M F A.Learning neighborhood discriminative manifolds for video-based facerecognition.Image Analysis and Processing-ICIAP 2011,Springer Berlin Heidelberg,2011:247-256.
  • 9Kim T K,Kittler J,Cipolla R.On-line learning of mutually orthogonal subspaces for face recognition by imagesets.Image Processing,IEEE Transactions on,2010; 19(4):1067-1074.
  • 10Sigal L,Balan A O,Black M J.Humaneva:synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated humanmotion.International Journal of Computer Vision,2010; 87(12):4-27.

引证文献1

中国图象图形学报
2012年 第12期

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