research-article

Dimensionality reduction based on nonparametric discriminant analysis with kernels for feature extraction and recognition

Authors:

Jeng-Shyang PanAuthors Info & Claims

ICUIMC '10: Proceedings of the 4th International Conference on Uniquitous Information Management and Communication

Article No.: 29, Pages 1 - 8

https://doi.org/10.1145/2108616.2108652

Published: 14 January 2010 Publication History

Abstract

Dimensionality reduction is the most popular method for feature extraction and recognition. Recently, Li et al. (IEEE PAMI, 2009) proposed Nonparametric Discriminant Analysis (NDA) based dimensionality reduction for face recognition and reported an excellent recognition performance. However, NDA has its limitations on extracting the nonlinear features of face images for recognition, and owing to the highly nonlinear and complex distribution of face images under a perceivable variation in viewpoint, illumination or facial expression. In order to increase the NDA, we extend the NDA with kernel trick to propose Nonparametric Kernel Discriminant Analysis (NKDA) for feature extraction and recognition. Experimental results on ORL, YALE and UMIST face databases show that NKDA outperforms NDA on recognition, which demonstrates that it is feasible to improve NDA with kernel trick for feature extraction.

References

[1]

Jun-Bao Li, Jeng-Shyang Pan, "A novel pose and illumination robust face recognition with a single training image per person algorithm", Chinese Optics Letters, 2008, 6(4): 255--257.

[2]

Dake Zhou Xin Yang Ningsong Peng, Face recognition using improved-LDA with facial combined feature, Chinese Optics Letters, 2005, 6(3): 330--332.

[3]

Xuerong Chen Zhongliang Jing Shaoyuan Sun Gang Xiao, Fusion of visible and infrared imagery for face recognition, Chinese Optics Letters, 2004, 2(12): 694--697.

[4]

Fukunaga, K., and Mantock, J. "Nonparametric discriminant analysis. IEEE Trans. Pattern Anal. Mach. Intell.", 1983, (5): 671--678.

Digital Library

[5]

Zhifeng Li, Dahua Lin and Xiaoou Tang. "Nonparametric Discriminant Analysis for Face Recognition". IEEE Trans. Pattern Anal. Mach. Intell., Apr. 2009, 31(4): 755--761.

Digital Library

[6]

Jun-Bao Li, Jeng-Shyang Pan and Zhe-Ming Lu. "Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models", Neural Comput & Applic., 6 (18): 613--621.

Digital Library

[7]

Jeng-Shyang Pan, Jun-Bao Li and Zhe-Ming Lu, "Adaptive Quasiconformal Kernel Discriminant Analysis", Neurocomputing. 2008, 71: 2754--2760.

Digital Library

[8]

Jun-Bao Li, Shu-Chuan Chu, Jeng-Shyang Pan and Jiun-Huei Ho. Adaptive Data-Dependent Matrix Norm Based Gaussian Kernel for Facial Feature Extraction. International Journal on Innovative Computing, Information and Control. 2007, 3(5): 1263--1272.

[9]

Ferdinando Samaria, Andy Harter, "Parameterisation of a Stochastic Model for Human Face Identification, Proceedings of 2nd IEEE Workshop on Applications of Computer Vision (1994) 138--142.

[10]

Daniel B Graham and Nigel M Allinson. Face Recognition: From Theory to Applications. Computer and Systems Sciences. Vol. 163, pp 446--456, 1998.

[11]

Jian Yang, Alejandro F. Frangi, Jing-yu Yang, David Zhang, and Zhong Jin, "KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 230--244, Feb.2005.

Digital Library

[12]

Qingshan Liu, Hanqing Lu, and Songde Ma, "Improving kernel Fisher discriminant analysis for face recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 14, no. 1, pp. 42--49, Jan. 2004.

Digital Library

[13]

V. Vapnik, "The Nature of Statistical Learning Theory," New York, Springer, 1995.

Digital Library

[14]

K. R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, "An introduction to kernel-based learning algorithms," IEEE Trans. Neural Networks, vol. 12, pp. 181--201, Mar. 2001.

Digital Library

[15]

B. Scholkopf, A. Smola, and K. R. Müller, "Nonlinear Component Analysis as a Kernel Eigenvalue Problem," Neural Computation, vol. 10, no. 5, pp. 1299--1319, 1998.

Digital Library

[16]

S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K.-R. Muller, "Fisher Discriminant Analysis with Kernels," Proc. IEEE Int'l Workshop Neural Networks for Signal Processing IX, pp. 41--48, Aug. 1999.

[17]

Juwei Lu, Konstantinos N. Plataniotis and Anastasios N. Venetsanopoulos, "Face recognition using kernel direct discriminant analysis algorithms", IEEE Transactions on Neural Networks, vol. 14, no. 1, pp.117--226, 2003.

Digital Library

[18]

G. Baudat and F. Anouar, "Generalized Discriminant Analysis Using a Kernel Approach," Neural Computation, vol. 12, no. 10, pp. 2385--2404, 2000.

Digital Library

[19]

Zhizheng Liang and Pengfei Shi, "Uncorrelated discriminant vectors using a kernel method", Pattern Recognition, vol. 38, pp. 307--310, 2005.

[20]

Z. Liang and P. Shi, "Efficient algorithm for kernel discriminant anlaysis", Pattern Recognition, vol. 37, no. 2 pp. 381--384, 2004.

[21]

Z. Liang, P. Shi, "An efficient and effective method to solve kernel Fisher discriminant analysis", Neurocomputing, vol. 61, pp. 485--493, 2004.

Digital Library

[22]

J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Face Recognition Using Kernel Direct Discriminant Analysis Algorithms," IEEE Trans. Neural Networks, vol. 14, no. 1, pp. 117--126, 2003.

Digital Library

[23]

M. H. Yang, "Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods," Proc. Fifth IEEE Int'l Conf. Automatic Face and Gesture Recognition, pp. 215--220, May 2002.

Digital Library

[24]

Wenming Zheng, Cairong Zou, and Li Zhao, "Weighted maximum margin discriminant analysis with kernels", Neurocomputing, vol. 67, pp. 357--362, 2005.

Digital Library

[25]

Jian Huang, Pong C Yuen, Wen-Sheng Chen and J H Lai. "Kernel Subspace LDA with Optimized Kernel Parameters on Face Recognition", Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004.

Digital Library

[26]

Lei Wang, Kap Luk Chan, and Ping Xue, "A Criterion for Optimizing Kernel Parameters in KBDA for Image Retrieval", IEEE Trans. Systems, Man and Cybernetics-Part B: Cybernetics, vol. 35, no. 3, pp. 556--562, June 2005.

Digital Library

[27]

Wen-Sheng Chen, Pong C. Yuen, Jian Huang, and Dao-Qing Dai, "Kernel Machine-Based One-Parameter Regularized Fisher Discriminant Method for Face Recognition", IEEE Trans. Systems, Man and Cybernetics-Part B: Cybernetics, vol. 35, no. 4, pp. 658--669, August 2005.

Digital Library

[28]

Daniel B Graham and Nigel M Allinson. Face Recognition: From Theory to Applications. Computer and Systems Sciences. Vol. 163, pp 446--456, 1998.

Cited By

Sharma SKumar M(2014)An Occluded Face Recognition Approach using Feature Specific KPCA ApproachProceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies10.1145/2677855.2677859(1-5)Online publication date: 14-Nov-2014
https://dl.acm.org/doi/10.1145/2677855.2677859

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cover image ACM Conferences

ICUIMC '10: Proceedings of the 4th International Conference on Uniquitous Information Management and Communication

January 2010

550 pages

ISBN:9781605588933

DOI:10.1145/2108616

Conference Chairs:
Kwan-Ho You
Sungkyunkwan University, Korea
,
Sang-Won Lee
Sungkyunkwan University, Korea
,
General Chairs:
Won Kim
Sungkyunkwan University, Korea
,
Dongho Won
SungKyunkwan University, Korea

Copyright © 2010 ACM.

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Published: 14 January 2010

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China Postdoctoral Science Foundation

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ICUIMC '10

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SIGKDD

ICUIMC '10: The 4th International Conference on Ubiquitous Information Management and Communication

January 14 - 15, 2010

Suwon, Republic of Korea

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Cited By

Sharma SKumar M(2014)An Occluded Face Recognition Approach using Feature Specific KPCA ApproachProceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies10.1145/2677855.2677859(1-5)Online publication date: 14-Nov-2014
https://dl.acm.org/doi/10.1145/2677855.2677859

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