A Similarity Learning Algorithm and Its Application to Face Recognition

doi:10.3969/j.issn.1000-3428.2014.06.038

Abstract

Abstract: Traditional similarity learning algorithms consider all training samples when constructing paired-samples, which would lead to a larger paired-sample space that depends on the training samples in a square fashion. It is well-known that it is time-consuming when Support Vector Machine(SVM) processes a large-scale classification problem. Aiming at this problem, this paper proposes an improved similarity learning algorithm using SVM, and applies it to face recognition. This paper introduces a new paired-sample construction method, called pairwise sample method. In order to speed up the training procedure, it adopts K Nearest Neighbor(KNN) algorithm to reduce the number of dissimilar paired-samples. In addition, the random projection method is introduced to reduce the dimensionality of face data. Experimental results show that the improved algorithm has better classification performance compared with algorithm based on difference space paired-sample and algorithm difference absolute value paired-sample.

摘要： 传统的支持向量机相似性学习算法在构造样本对时，会考虑所有的原始训练样本，致使样本对空间和原样本空间呈平方关系，而过多的训练样本对会降低训练速度。为此，提出一种改进的支持向量机相似性学习算法，并应用到人脸识别中。引入二元样本对方法构造样本对，采用K近邻算法减少不相似样本对的生成，从而加快支持向量机的训练速度，同时使用随机降维方法来降低人脸数据的维数。实验结果表明，与基于差空间样本对和差绝对值样本对的算法相比，该算法可获得更高的识别率。

关键词: 相似性学习, 样本对, 支持向量机, K近邻算法, 随机降维, 人脸识别

CLC Number:

TP18

XIA Pei-pei, ZHANG Li. A Similarity Learning Algorithm and Its Application to Face Recognition[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2014.06.038.

夏佩佩，张莉. 一种相似性学习算法及其在人脸识别中的应用[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2014.06.038.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.3969/j.issn.1000-3428.2014.06.038

http://www.ecice06.com/EN/Y2014/V40/I6/175

References

参考文献 [1] Cortes C, Vapnik V. Support-vector Networks[J]. Machine Learning, 1995, 20(3): 273-297. [2] Cover T, Hart P. Nearest Neighbor Pattern Classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27. [3] Abdi H, Williams L J. Principal Component Analysis[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2010, 2(4): 433-459. [4] Roweis S, Saul L. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science, 2000, 290(5500): 2323- 2326. [5] Tenenbaum J B, de Silva V, Langford J C. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science, 2000, 290(5500): 2319-2323. [6] Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation[J]. Neural Computation, 2003, 15(6): 1373-1396. [7] Xing E P, Ng A Y, Jordan M I, et al. Distance Metric Learning with Application to Clustering with Side-information[C]//Proc. of NIPS’02. Cambridge, USA: MIT Press, 2002: 505-512. [8] Alipanahi B, Biggs M, Ghodsi A. Distance Metric Learning Vs. Fisher Discriminant Analysis[C]//Proc. of the 23rd Interna- tional Conference on Artificial Intelligence. [S. l.]: AAAI Press, 2008: 598-603. [9] Mika S, R?tsch G, Weston J, et al. Fisher Discriminant Analysis with Kernels[C]//Proc. of IEEE International Workshop on Neural Networks for Signal Processing IX. [S. l.]: IEEE Press, 1999: 41-48. [10] Goldberger J, Roweis S, Hinton G, et al. Neighbourhood Component Analysis[C]//Proc. of NIPS’04. Cambridge, USA: MIT Press, 2004: 513-520. [11] Hastie T, Tibshirani R. Discriminant Adaptive Nearest- neighbor Classification[J]. IEEE Transactions on PAMI, 1996, 18(6): 607-617. [12] Kwok J T, Tsang I W. Learning with Idealized Kernels[C]// Proc. of International Conference on Machine Learning. Washington D. C., USA: [s. n.], 2003: 400-407. [13] Tsang I W, Cheung P M, Kwok J T. Kernel Relevant Component Analysis for Distance Metric Learning[C]//Proc. of IJCNN’05. Montreal, Canada: [s. n.], 2005: 954-959. [14] Sugiyama M. Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction[C]//Proc. of Interna- tional Conference on Machine Learning. [S. l.]: AAAI Press, 2006: 927-934. [15] Bar-Hillel A, Hertz T, Shental N, et al. Learning a Mahalanobis Metric from Equivalence Constraints[J]. Journal of Machine Learning Research, 2005, 6(6): 937-965. [16] Phillips P J. Support Vector Machines Applied to Face Recognition[C]//Kearns M S, Solla S A, Cohn D A. Proc. of NIPS’99. Cambridge, USA: MIT Press, 1999: 204-210. [17] Melacci S, Sarti L, Maggini M, et al. A Neural Network Approach to Similarity Learning[C]//Proc. of the 3rd International Workshop on Artificial Neural Networks in Pattern Recognition. Berlin, Germany: Springer-Verlag, 2008: 133-136. [18] Burges C J C. A Tutorial on Support Vector Machines for Pattern Recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167. [19] 张莉. 支撑矢量机与核方法研究[D]. 西安: 西安电子科技大学, 2002. [20] Belhumeur P N, Hespanha J P, Kriegman D. Eigenfaces Versus Fisherfaces: Recognition Using Class Specific Linear Pro- jection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720. [21] Wright J, Yang A Y, Ganesh A, et al. Robust Face Recognition via Sparse Representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227. [22] Zhang Li, Zhou Weida, Chang P C, et al. Kernel Sparse Representation-based Classifier[J]. IEEE Transactions on Signal Processing, 2012, 60(4): 1684-1695. 编辑金胡考

Please choose a citation manager

Content to export