A Codebook Design Method for Robust VQ-Based Face Recognition Algorithm

Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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DOI: 10.4236/jsea.2010.32015                

Abstract

In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.

Keywords

Face Recognition, Vector Quantization (VQ), Codebook Design, Code Classification, Histogram Method

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	K. W. Bowyer, “Face recognition technology and the security versus privacy tradeoff,” IEEE Technology and Society, pp. 9–20, Spring 2004.
[2]	M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71–86, March 1991.
[3]	L. Wiskott, J. M. Fellous, N. Kruger, and C. von der Mals-burg, “Face recognition by elastic bunch graph matching,” IEEE Transactions Pattern Analysis and Machine Intelli-gence, Vol. 19, No. 7, pp. 775–779, July 1997.
[4]	R. Brunelli and T. Poggio, “Face recognition: Features versus templates,” IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 15, No. 10, pp. 1042–1052, October 1993.
[5]	P. S. Penev and J. J. Atick, “Local feature analysis: A general statistical theory for object representation,” Net-work: Computation in Neural Systems, Vol. 7, No. 3, pp. 477–500, 1996.
[6]	R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: A survey,” Proceedings IEEE, Vol. 83, No. 5, pp. 705–740, May 1995.
[7]	S. Z. Li and A. K. Jain, “Handbook of face recognition,” Springer, New York, 2005.
[8]	F. Goudail, E. Lange, T. Iwamoto, K. Kyuma, and N. Otsu, “Face recognition system using local autocorrela-tions and multi-scale integration,” IEEE Transactions Pat-tern Analysis and Machine Intelligence, Vol. 18, No. 10, pp. 1024–1028, 1996.
[9]	K. M. Lam and H. Yan, “An analytic-to-holistic approach for face recognition based on a single frontal view,” IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 20, No. 7, pp. 673–686, 1998.
[10]	B. Moghaddam and A. Pentland, “Probabilistic visual learning for object representation,” IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 696–710, 1997.
[11]	W. Zhao, “Discriminant component analysis for face recog-nition,” Proceedings ICPR’00, Track 2, pp. 822–825, 2000.
[12]	M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Transactions on Neural Networks, Vol. 13, No. 6, pp. 1450–1464, 2002.
[13]	S. G. Karungaru, M. Fukumi, and N. Akamatsu, “Face recognition in color images using neural networks and genetic algorithms,” International Journal of Computa-tional Intelligence and Applications, Vol. 5, No. 1, pp. 55–67, 2005.
[14]	S. Aly, N. Tsuruta, and R. Taniguchi, “Face recognition under varying illumination using Mahalanobis self-orga-nizing map,” Artificial Life and Robotics, Vol. 13, No. 1, pp. 298–301, 2008.
[15]	K. Kotani, Q. Chen, and T. Ohmi, “Face recognition using vector quantization histogram method,” IEEE 2002 Interna-tional Conference on Image Processing, II–105–108, 2002.
[16]	A. Gersho and R. M. Gray, “Vector quantization and signal compression,” Kluwer Academic, 1992.
[17]	T. Nakayama, M. Konda, K. Takeuchi, K. Kotani, and T. Ohmi, “Still image compression with adaptive resolution vector quantization technique,” International Journal of Intelligent Automation and Soft Computing, Vol. 10, No. 2, pp. 155–166, 2004.
[18]	P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, “The FERET database and evaluation procedure for face rec-ognition algorithms,” Image and Vision Computing Journal, Vol. 16, No. 5, pp. 295–306, 1998.
[19]	AT&T Laboratories Cambridge, “The database of faces,” at: http://www.cl.cam.ac.uk/research/dtg/attarchive/face database.html
[20]	T. Kohonen, “Self-Organizing Maps,” Springer, USA, 1995.
[21]	Q. Chen, K. Kotani, F. Lee, and T. Ohmi, “Face recogni-tion using codebook designed by code classification,” 2006 IEEE International Conference on Signal and Image Processing, Hubli, India, pp. 397–401, December 2006.
[22]	Q. Chen, K. Kotani, F. Lee, and T. Ohmi, “A VQ-based fast face recognition algorithm using optimized code-book,” Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, pp. 298–303, August 2008.
[23]	Q. Chen, K. Kotani, F. Lee, and T. Ohmi, “VQ-based face recognition algorithm using code pattern classification and Self-Organizing Maps,” Proceedings of 9th Interna-tional Conference on Signal Processing (ICSP’08), Bei-jing, pp. 2059–2064, October 2008.