期刊文献+

无关性判别保局算法及其在人脸识别中的应用 被引量:3

UDLP:an algorithm based on uncorrelated discriminant locality preserving and its application in face recognition
原文传递
导出
摘要 特征提取是人脸识别过程中的一个重要步骤,是人脸识别算法有效性的关键。提出了一种基于无关性判别保局的特征提取算法,并应用于人脸识别。基于保局投影算法的人脸识别是一种有效的人脸识别算法,但它只考虑了数据的局部性,没有考虑类别信息,也没有考虑所提特征之间的相关性,现有的改进算法虽然考虑了类别信息,但是没有考虑到类间信息。本文算法使得所提特征之间相互无关,这样降低了数据冗余,同时考虑到类别信息,使得投影后的类间区分度加强了。实验结果验证了算法的正确性和有效性,比传统算法有较好的识别性能。 Feature extraction is an important and critical step in the process of face recognition, a feature extraction algorithm based on uncorrelated discriminant locality preserving is proposed, and its application in face recognition is carried out. Laplaeianface which is based on locality preserving projection is an effective algorithm, but it only takes the locality into account, thus the extracted features might be highly correlated, and it does not consider the class information. Some improved algorithms consider the discrimiant information, but the interclass information is not considered. The algorithm proposed here not only imposes an uncorrelated constraint to reduce data redundancy, but also utilizes the class information and the interclass separability after projection is enhanced. Experiments validate the correctness and effectiveness of the algorithm, and prove that the proposed algorithm has better performance.
出处 《中国图象图形学报》 CSCD 北大核心 2011年第1期66-71,共6页 Journal of Image and Graphics
关键词 特征提取 保局投影 无关性 人脸识别 feature extraction locality preserving projection uncorrelated-ness face recognition
  • 相关文献

参考文献15

  • 1Zhao W, Chellappa R, Phillips P J, et al. Face recognition: a literature survey [J]. ACM Computing Survey, 2003, 12: 399- 458.
  • 2Turk M, Penlland A. Eigenfaces for recognition [ J]. Cognilive Neurosci, 1991, 3 (1): 71-86.
  • 3Belhumeur P N, Kriegman D J. Eigenfaces vs fisherfaces: recognition using class specific linear projection [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19: 711-720.
  • 4He X, Niyogi P, Han J. Face recognition using laplacianfaces [ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
  • 5Jolliffe I T. Principal Component Analysis, Series: Springer Series in Statistics[ M]. 2nd ed, NY:Springer, 2002: 487-492.
  • 6Duda R, Hart P. Pattern Classification and Scene Analysis[ M]. New York: Wiley Press, 1973: 114-117.
  • 7He X F, Niyogi P. Locality preserving projections [ C ]// Advances in Neurat Information Processing Systems. Cambridge: MIT Press, 2004: 327-334.
  • 8Tenenbaum J B, Silva V de, Langford J C. A global geometric framework for nonlinear dimensionality reduction [ J]. Science, 2000,290(5500) : 2319-2323.
  • 9Rowies S, Saul L. Nonliear dimensionality reduction by locally linear embedding[ J]. Science, 2000, 290 (5500) : 2323-2326.
  • 10Belkin M, Niyogo P. Laplacian eigenmaps for dimensionality reduction and data representation [ J ]. Neural Computation, 2003,15(6) : 1373-1396.

二级参考文献8

  • 1He X, Yan S, Hu Y, Niyogi P, and Zhang H J. Face recognition using Laplacian faces. IEEE Trans. on Pattern Anal. Machine Intelli., 2005, 27(3): 328-340.
  • 2Turk M A and Pentland A P. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.
  • 3Sam T, Roweis and Saul K L. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326.
  • 4He X and Niyogi. Locality preserving projections. Proceedings of Advances In Neural Information Processing Systems 16, MA: Cambridge, MIT Press, 2004: 153-160.
  • 5Zhao Haitao, Sun Shaoyuan, Jing Zhongliang, and Jingyu Yang. Local structure based supervised feature extraction, Pattern Recognition, 2006, 39(88): 1546-1550.
  • 6Roweis S, Saul L, and Hinton G. Global coordination of local linear models. Proceedings of Advances in Neural Information Processing System 14, MA: Cambridge, MIT Press, 2001: 889-896.
  • 7Hallinan P. A deformable model for face recognition under arbitrary lighting conditions. [PHD thesis]. Havard Univ, 1995.
  • 8Graham D B and Allinson N M. Characterizing virtual eigensignatures for general purpose face recognition. In: Wechsler H., Phillips P.J., Bruce V., Fogelman-Soulie F., Huang T.S. eds.. Face Recognition: From Theory to Applications. NATO ASI Series F, Computer and Systems Sciences, 1998, 163: 446-456.

共引文献33

同被引文献40

  • 1TURK M A, PENTLAND A P. Face recognition using eigenfaces[C ] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Maui: IEEE Computer Society, 1991:586 - 591.
  • 2BELHUMEUR P N ,HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. Fisherface: Recognition using class specific linear projection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7) :711 -720.
  • 3RAUDYS S J,JAIN A K. Small sample size effects in statistical pat- tern recognition : Recommendation for practitioners [ J ]. IEEE Transactions on Pattern Analysis And Machine Inteligence, 1991,13 (3) : 252 - 264.
  • 4YU H, YANG J. A direct LDA algorithm for high-dimensional data with application to face recognition [J ]. Pattern Recognition, 2001, 34(10) :2067 -2070.
  • 5LU J, PLATANIOTISK K N, VENETSANOPOULOS A N. Regulari- zation studies of linear discriminant analysis in small sample size scenarios with application to face recognition [ J ]. Pattern Recogni- tion Letters,2005,26 ( 2 ) : 181 - 191.
  • 6LI H F, JIANG T, ZHANG K S. Efficient and robust feature extration by maximum margin criterion [ J ]. IEEE Transactions on Neural Networks ,2006,17 ( 1 ) : 157 - 165.
  • 7ROWEIS S T, SAUL L K. Nonlinear dimensionality reduction by lo- cally linear embedding [ J ]. Science,2000,290:2323 - 2326.
  • 8BELKIN M, NIYOGI P. Laplacian eigenmaps for dimensionality re- duction and data representation [ J ]. Neural Computation, 2003,15 (6) :1373 - 1396.
  • 9TENENBAUM J B, SILVE V D, LANGFORD J C. A global geo- metric framework for nonlinear dimensionality reduction [ J ]. Sci- ence ,2000,290:2319 - 2323.
  • 10HE X F, YAN S C, HU Y X, et al. Face recognition using lapla- cianfaces[ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27 ( 3 ) : 328 - 340.

引证文献3

二级引证文献14

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