A Fuzzy Neuro Clustering Based Vector Quantization for Face Recognition
- 1.2k Downloads
A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame. In this paper, an improved codebook design method is proposed for Vector Quantization (VQ)-based face recognition which improves recognition accuracy. A codebook is created by combining a systematically organized codebook based on the classification of code patterns and another codebook created by Integrated Adaptive Fuzzy Clustering (IAFC) method. IAFC is a fuzzy neural network which incorporates a fuzzy learning rule into a neural network. The performance of proposed algorithm is demonstrated by using publicly available AT&T database and Yale database. Experimental results show face recognition using the proposed codebook is more efficient yielding a rate of 99.25% for AT & T and 98.18% for Yale which is higher than most of the existing methods.
KeywordsFace Recognition Vector Quantization Codebook Integrated Adaptive Fuzzy Clustering Self Organization Map
Unable to display preview. Download preview PDF.
- 5.Moghaddam, B., Nastar, C., Pentland, A.: A Bayesian similarity measure for direct image matching. In: Proceedings, International Conference on Pattern Recognition (1996)Google Scholar
- 6.Phillips, P.J.: Support vector machines applied to face fecognition. Advanced Neural Information Processing Systems 11, 803–809 (1998)Google Scholar
- 11.Kotani, K., Chen, Q., Ohmi, T.: Face recognition using vector quantization histogram method. In: Proceedings of the 2002 Int. Conf. on Image Processing vol. II(III), pp. II–105–II–108 (2002)Google Scholar
- 12.Chen, Q., Kotani, K., Lee, F.F., Ohmi, T.: A VQ based fast face recognition algorithmn using optimized codebook. In: Proeedings of the 2008 Int. Conf. on Wavelet Analysis and Pattern Recognition (2008)Google Scholar
- 15.Chen, Q., Kotani, K., Lee, F.F., Ohmi, T.: Face recognition using codebook designed by code classification. In: IEEE Int. Conf. on Signal and Image Processing, pp. 397–401 (2006)Google Scholar
- 16.Kohonen, T.: The Self-Organizing Maps. Proceedings of the IEEE 78(9) (September 1990)Google Scholar
- 17.Kim, Y.S., Mitra, S.: An adaptive integrated fuzzy clustering model for pattern recognition. Journal Fuzzy Sets and Systems (65), 297–310 (1994)Google Scholar
- 18.AT & T. The Database of Faces, http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
- 19.Yale Face Database, http://cvc.yale.edu/projects/yalefaces/yalefaces.html