The field of face recognition is increasingly investigated for access control, face based search, passport processing, security, surveillance, etc. applications. Performance of face recognition systems under constrained environment is quite satisfactory, but face recognition in unconstrained environment is yet a challenging problem due to key technical challenging issues. Varying illumination is one of the key issues in real time face recognition applications. Experimental assessment of various methods developed by research community demonstrates that, yet there is a need and scope for improving methods to handle the varying illumination problem. In this paper, a novel approach, referred to as fuzzy threshold based local binary pattern is proposed for extracting illumination invariant features. Local binary pattern based method is modified by introducing a fuzzy based threshold for generating binary pattern. Effectiveness of proposed method is assessed on extended Yale B face database. Experimental results demonstrate that proposed method performs better than conventional binary pattern under complex illumination conditions.

1. T. Ahonen, A. Hadid, and M. Pietikainen, “Face Description with Local Binary Patterns: Application to Face Recognition, “IEEE Transactions on Pattern Analysis and Machine Intelligence,” Vol. 28, No. 12, December 2006.
2. 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.
3. J. C. Bezdek, J. Keller, R. Krisnapuram, and N. R. Pal, “Fuzzy Models and Algorithms for Pattern Recognition and Image Processing,” Springer, 2005.
4. S. Chen, B. C. Lovell and T. Shan, “Robust Adapted Principal Component Analysis for Face Recognition,” International Journal of Pattern Recognition and Artificial Intelligence, 23(3), 491-520, May 2009.
5. Y. Gang, L. Jiawei, L. Jiayu, MA Qingli, YU Ming, “Illumination Variation in Face Recognition: A Review,” Second International Conference on Intelligent Networks and Intelligent Systems, Tianjian, China, December 2009.
6. Z. Guo, L. Zhang, D. Zhang, and X. Mou, “Hierarchical Multiscale LBP for Face and Palmprint Recognition,” Int. Conf. on Image Processing, 26-29 September, 2010.
7. A. K. Jain and S. Z. Li, “Handbook of Face Recognition”, Springer, 2005.
8. K. Lee, J. Ho, and D. Kriegman, “Acquiring Linear Subspaces for Face Recognition under Variable Lighting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 5, May 2005.
9. N. Kela, A. Rattani and P. Gupta, “Illumination Invariant Elastic Bunch Graph Matching for Efficient Face Recognition,” Computer Vision and Pattern Recognition Workshop, IEEE Computer Society, Los Alamitos, CA, USA, 2006.
10. R. Makwana, V. Thakar, and N. chauhan, “Fuzzy Threshold based Local Binary Pattern for Illumination Invariant Face Recognition”, International Conference on Signal System and Automation, GCET, V.V. Nagar, January, 2011.
11. A. Martez, A. Kak, “PCA versus LDA,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.23, no. 2, February 2001, pp.228-233.
12. T. Ojala, M. Pietikainen, and T. Maenpaa, “Multi-resolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns, “IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, July 2002.
13. Y. Park, S. Park, and J. Kim, “Retinex method based on adaptive smoothing for illumination invariant face recognition,” Signal Processing archive, Vol. 88, No. 8, 2008.
14. P. Phillips, “FRVT 2006 and ICE 2006 large-scale results,” National Institute of Standards and Technology, 2007.
15. X. Qiong, L. B. cheng and W. Bo, “Face Recognition by Fast Independent Component Analysis and Genetic Algorithm,” Proc. of the Fourth Int. Conf. on Computer and Information Technology, October 2004.
16. Y. Rodriguez and S. Marcel, “Face Authentication Using Adapted Local Binary Pattern Histograms,” Lecture Notes in Computer Science, 2006, Volume 3954/2006, 321-332.
17. T. Ross, “Fuzzy Logic with Engineering Application”, Wiley Publisher, second Edition.
18. T. Sim, S. Baker and M. Bsat, “The CMU Pose, Illumination, and Expression Database,” IEEE Transactions on Pattern Analysis and Machine Intelligence,” Vol. 25, No. 12, 1615-1618, 2003.
19. V. Struc and N. Pavesi´c, "Gabor-Based Kernel Partial-Least-Squares Discrimination Features for Face Recognition", Informatica (Vilnius), vol. 20, no. 1, pp. 115-138, 2009.
20. H. R. Tizhoosh, “Fuzzy Image Processing: Potentials and State Of The Art”, 5th International Conference on Soft Computing, Iizuka, Japan, October 16-20, 1998, Vol. 1, Pp. 321-324.
21. X. Wu, L. Gu, S. Wang, J. Yang , Y. Zheng, and D. Yu, “Fuzzy Kernel Discriminant Analysis and Its application to Face Recognition, Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, Beijing, China, 118-123, November 2007.
22. D. Zhang, Z. Guo, and L. Zhang, “A Completed Modeling of Local Binary Pattern Operator for Texture Classification,” IEEE Transactions on Image Processing, 2010.
23. T. Zhang, T. Tang, B. Feng, Z. Shang, and X. Liu, “Face Recognition under Varying Illumination Using Gradientfaces,” IEEE Transactions on Image Processing, volume 18, No. 11, 2599-2606, November 2009.
24. X. Zou, J. Kittler and K. Messer, “Illumination Invariant Face Recognition: A Survey,” First IEEE International Conference on Biometrics Theory Applications and Systems, 2007.

Face Recognition Feature Extraction Fuzzy Threshold Illumination Local Binary Pattern