Face Recognition Based On Vector Quantization Using Fuzzy Neuro Clustering
A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.
[1] R. Chellappa., C. L. Wilson., S. Sirohey: "Human and machine recognition of faces: a survey”. Proc. IEEE, 83(5), pp. 705—740, 1995.
[2] S. Z. Li., A. K. Jain.: "Handbook of Face Recognition”, Springer, New York, 2005.
[3] J. R. Solar, P. Navarreto, " Eigen space-based face recognition: a comparative study of different approaches, IEEE Tran., Systems man And Cybernetics- part c: Applications, Vol. 35,No. 3, 2005.
[4] Sahoolizadeh, H.; Ghassabeh, Y.A.;” Face recognition using eigen-faces, fisher-faces and neural networks, Cybernetic Intelligent Systems, 2008. CIS 2008.7th IEEE International Conference on 9-10 Sept. 2008
[5] M.Turk., A. Pentland.: "Eigenfaces for recognition”. Journal of Cognitive Neuroscience, 3(1), pp.71--86, 1991
[6] A. A. Thomas and M. Wilscy, "Face Recognition Using Simplified Fuzzy ARTMAP”, J.Signal and Image Processing, Vol.1, No.2, pp 134-136, December 2010.
[7] M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face Recognition by Independent Component Analysis, IEEE Trans. on Neural Networks, Vol. 13, No. 6, November 2002, pp. 1450-1464
[8] C. Liu, H. Wechsler, Evolutionary Pursuit and Its Application to Face Recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, No. 6, June 2000, pp. 570-582
[9] L. Wiskott, J.-M. Fellous, N. Krueuger, C. von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 775-779
[10] P. J.Phillips.: "Support vector machines applied to face recognition”. Advanced Neural Information. Processing. Systems. 11, 803–809, 1998.
[11] W. Zhao., R. Chellappa., P. J. Phillips., A. Rosenfeld.: "Face Recognition: A Literature Survey”. ACM Computing Surveys, Vol. 35, No. 4, pp. 399–458, 2003.
[12] A.V. Nefian, M.H. Hayes, Hidden Markov Models for Face Recognition, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'98, Vol. 5, 12-15 May 1998, Seattle, Washington, USA, pp. 2721-2724
[13] R. Brunelli., T. Poggio .: "Face recognition: features versus templates”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10), pp.1042—1052, 1993.
[14] P. S. Penev., J. J. Atick.: "Local Feature Analysis: A general statistical theory for object representation”. Network: Computation in Neural Systems, 7(3), pp.477—500, 1996.
[15] Goudail., E. Lange., T. Iwamoto., K. Kyuma., N. Otsu. : "Face recognition system using local autocorrelations and multiscale integration. IEEE Transaction on Pattern Analysis and Machine Intelligence, 18(10), pp. 1024—1028, 1996.
[16] K Sayood.: "Introduction to Data Compression”. Morgan Kaufmann, San Francisco, 2000.
[17] K. Kotani., Q. Chen., and T. Ohmi.: "Face recognition using vector quantization histogram method”. Proceedings of the 2002 Int. Conf. on Image Processing, Vol. II of III:II- 105-II-108, 2002
[18] Q. Chen., K. Kotani., F.F. Lee., and T. Ohmi.: "A VQ based fast face recognition algorithm using optimized codebook”. Proeedings of the 2008 Int. Conf. on Wavelet Analysis and Pattern Recognition, 2008.
[19] T. Kohonen.: "The Self-Organizing Maps”. Proceedings of the IEEE, Vol 78, No 9, September, 1990.
[20] Y. S. Kim., S Mitra., "An adaptive integrated fuzzy clustering model for pattern recognition”. Journal. Fuzzy Sets and Systems.Vol (65) pp 297--310, Elsevier, Netherlands, 1994
[21] T. Nakayama., M. Konda., K. Takeuchi., K. Kotani., T. Ohmi.: "Still image compression with adaptive resolution vector quantization technique”. Int. Journal of Intelligent Automation and Soft Computing, 10(2), pp.155—166, 2004
[22] Q. Chen., K. Kotani., F. F. Lee., and T. Ohmi.: "Face recognition using codebook designed by code classification”. IEEE Int. Conf. on Signal and Image Processing, pp. 397—40,2006.
[23] AT&T, "The Database of Faces”. http://www.cl.cam.ac.uk/research/ dtg/attarchive/facedatabase.html
[24] YaleFaceDatabase. http://cvc.yale.edu/projects/yalefaces/yalefaces.html
[25] TheIndianFaceDatabase.http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/
[26] B. Liu, B. Liu, X. Sun, and J. Zhang, Eigenface classification using an extended kernel-based nonlinear discriminator, International Conference on Communications, Circuits and Systems, Volume 2, pp. 1123-1126, 2004.
[27] Y. Li, Z. Mu, X. Xu, Multimodal recognition based on face and ear International Conference on Wavelet Analysis and Pattern Recognition, Volume 3, pp. 1203 - 1207, 2007.
[28] C. Xu, H. Jiang, J. Yu, Robust two-dimensional principle component analysis, 27th Chinese Control Conference, pp.452-455, 2008.
[29] Z. M. Hafed and M. D. Levine, Face recognition using the discrete cosine transform, International Journal of Computer Vision, 43(3),2001.
[30] S. L. G. Guo and K. Chan, Face recognition by support vector machines, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 196-201, 2000.
[31] D. L. Dong-mei., S.Z. Qiu., "Bag-of-Words Vector Quantization Based Face Identification”, IEEE Second International Symposium on Electronic Commerce and Security, 2009
[32] J. Yang., D. Zhang., A. F. Frangi., and Jing-yu Yang., "Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 26, pp 131-137, January 2004
[33] M.H. Yang, "Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods,” Proc. Fifth IEEE Int’l Conf. Automatic Face and Gesture Recognition (RGR’02), pp. 215-220, May 2002.
[34] K.Jaya Priya., R.S Rajesh, "A Local Min-Max Binary Pattern Based Face Recognition Using Single Sample per Class”, International Journal of Advanced Science and Technology Vol. 36, pp 41-49 November, 2011
[35] V More., A Wagh, "Improved Fisher Face Approach for Human Recognition System using Facial Biometrics”, International Journal of Information and Communication Technology Research, Volume 2, pp 135-139, February 2012.
[36] D.Sudarshan S.,D Pooja S, "Principle Component Analysis of Low Level Feature Based Face Recognition System”, International Journal of Recent Trends in Engineering, Vol 2, No. 3, pp 149-151, November 2009
[37] E A Daoud , "Enhancement of the Face Recognition Using a Modified Fourier-Gabor Filter”, Int. J. Advance. Soft Comput. Appl., Vol. 1, No. 2,pp 119-131 November 2009
[1] R. Chellappa., C. L. Wilson., S. Sirohey: "Human and machine recognition of faces: a survey”. Proc. IEEE, 83(5), pp. 705—740, 1995.
[2] S. Z. Li., A. K. Jain.: "Handbook of Face Recognition”, Springer, New York, 2005.
[3] J. R. Solar, P. Navarreto, " Eigen space-based face recognition: a comparative study of different approaches, IEEE Tran., Systems man And Cybernetics- part c: Applications, Vol. 35,No. 3, 2005.
[4] Sahoolizadeh, H.; Ghassabeh, Y.A.;” Face recognition using eigen-faces, fisher-faces and neural networks, Cybernetic Intelligent Systems, 2008. CIS 2008.7th IEEE International Conference on 9-10 Sept. 2008
[5] M.Turk., A. Pentland.: "Eigenfaces for recognition”. Journal of Cognitive Neuroscience, 3(1), pp.71--86, 1991
[6] A. A. Thomas and M. Wilscy, "Face Recognition Using Simplified Fuzzy ARTMAP”, J.Signal and Image Processing, Vol.1, No.2, pp 134-136, December 2010.
[7] M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face Recognition by Independent Component Analysis, IEEE Trans. on Neural Networks, Vol. 13, No. 6, November 2002, pp. 1450-1464
[8] C. Liu, H. Wechsler, Evolutionary Pursuit and Its Application to Face Recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, No. 6, June 2000, pp. 570-582
[9] L. Wiskott, J.-M. Fellous, N. Krueuger, C. von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 775-779
[10] P. J.Phillips.: "Support vector machines applied to face recognition”. Advanced Neural Information. Processing. Systems. 11, 803–809, 1998.
[11] W. Zhao., R. Chellappa., P. J. Phillips., A. Rosenfeld.: "Face Recognition: A Literature Survey”. ACM Computing Surveys, Vol. 35, No. 4, pp. 399–458, 2003.
[12] A.V. Nefian, M.H. Hayes, Hidden Markov Models for Face Recognition, Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP'98, Vol. 5, 12-15 May 1998, Seattle, Washington, USA, pp. 2721-2724
[13] R. Brunelli., T. Poggio .: "Face recognition: features versus templates”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10), pp.1042—1052, 1993.
[14] P. S. Penev., J. J. Atick.: "Local Feature Analysis: A general statistical theory for object representation”. Network: Computation in Neural Systems, 7(3), pp.477—500, 1996.
[15] Goudail., E. Lange., T. Iwamoto., K. Kyuma., N. Otsu. : "Face recognition system using local autocorrelations and multiscale integration. IEEE Transaction on Pattern Analysis and Machine Intelligence, 18(10), pp. 1024—1028, 1996.
[16] K Sayood.: "Introduction to Data Compression”. Morgan Kaufmann, San Francisco, 2000.
[17] K. Kotani., Q. Chen., and T. Ohmi.: "Face recognition using vector quantization histogram method”. Proceedings of the 2002 Int. Conf. on Image Processing, Vol. II of III:II- 105-II-108, 2002
[18] Q. Chen., K. Kotani., F.F. Lee., and T. Ohmi.: "A VQ based fast face recognition algorithm using optimized codebook”. Proeedings of the 2008 Int. Conf. on Wavelet Analysis and Pattern Recognition, 2008.
[19] T. Kohonen.: "The Self-Organizing Maps”. Proceedings of the IEEE, Vol 78, No 9, September, 1990.
[20] Y. S. Kim., S Mitra., "An adaptive integrated fuzzy clustering model for pattern recognition”. Journal. Fuzzy Sets and Systems.Vol (65) pp 297--310, Elsevier, Netherlands, 1994
[21] T. Nakayama., M. Konda., K. Takeuchi., K. Kotani., T. Ohmi.: "Still image compression with adaptive resolution vector quantization technique”. Int. Journal of Intelligent Automation and Soft Computing, 10(2), pp.155—166, 2004
[22] Q. Chen., K. Kotani., F. F. Lee., and T. Ohmi.: "Face recognition using codebook designed by code classification”. IEEE Int. Conf. on Signal and Image Processing, pp. 397—40,2006.
[23] AT&T, "The Database of Faces”. http://www.cl.cam.ac.uk/research/ dtg/attarchive/facedatabase.html
[24] YaleFaceDatabase. http://cvc.yale.edu/projects/yalefaces/yalefaces.html
[25] TheIndianFaceDatabase.http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/
[26] B. Liu, B. Liu, X. Sun, and J. Zhang, Eigenface classification using an extended kernel-based nonlinear discriminator, International Conference on Communications, Circuits and Systems, Volume 2, pp. 1123-1126, 2004.
[27] Y. Li, Z. Mu, X. Xu, Multimodal recognition based on face and ear International Conference on Wavelet Analysis and Pattern Recognition, Volume 3, pp. 1203 - 1207, 2007.
[28] C. Xu, H. Jiang, J. Yu, Robust two-dimensional principle component analysis, 27th Chinese Control Conference, pp.452-455, 2008.
[29] Z. M. Hafed and M. D. Levine, Face recognition using the discrete cosine transform, International Journal of Computer Vision, 43(3),2001.
[30] S. L. G. Guo and K. Chan, Face recognition by support vector machines, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 196-201, 2000.
[31] D. L. Dong-mei., S.Z. Qiu., "Bag-of-Words Vector Quantization Based Face Identification”, IEEE Second International Symposium on Electronic Commerce and Security, 2009
[32] J. Yang., D. Zhang., A. F. Frangi., and Jing-yu Yang., "Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 26, pp 131-137, January 2004
[33] M.H. Yang, "Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods,” Proc. Fifth IEEE Int’l Conf. Automatic Face and Gesture Recognition (RGR’02), pp. 215-220, May 2002.
[34] K.Jaya Priya., R.S Rajesh, "A Local Min-Max Binary Pattern Based Face Recognition Using Single Sample per Class”, International Journal of Advanced Science and Technology Vol. 36, pp 41-49 November, 2011
[35] V More., A Wagh, "Improved Fisher Face Approach for Human Recognition System using Facial Biometrics”, International Journal of Information and Communication Technology Research, Volume 2, pp 135-139, February 2012.
[36] D.Sudarshan S.,D Pooja S, "Principle Component Analysis of Low Level Feature Based Face Recognition System”, International Journal of Recent Trends in Engineering, Vol 2, No. 3, pp 149-151, November 2009
[37] E A Daoud , "Enhancement of the Face Recognition Using a Modified Fourier-Gabor Filter”, Int. J. Advance. Soft Comput. Appl., Vol. 1, No. 2,pp 119-131 November 2009
@article{"International Journal of Information, Control and Computer Sciences:66477", author = "Elizabeth B. Varghese and M. Wilscy", title = "Face Recognition Based On Vector Quantization Using Fuzzy Neuro Clustering", abstract = "A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. A lot of algorithms have been proposed for face recognition. Vector Quantization (VQ) based face recognition is a novel approach for face recognition. Here a new codebook generation for VQ based face recognition using Integrated Adaptive Fuzzy Clustering (IAFC) is proposed. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a competitive neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database, Yale database, Indian Face database and a small face database, DCSKU database created in our lab. In all the databases the proposed approach got a higher recognition rate than most of the existing methods. In terms of Equal Error Rate (ERR) also the proposed codebook is better than the existing methods.
", keywords = "Face Recognition, Vector Quantization, Integrated Adaptive Fuzzy Clustering, Self Organization Map.", volume = "7", number = "11", pages = "1525-10", }
