Share This Article:

Illumination Invariant Face Recognition Using Fuzzy LDA and FFNN

Abstract Full-Text HTML XML Download Download as PDF (Size:452KB) PP. 45-50
DOI: 10.4236/jsip.2012.31007    4,452 Downloads   7,688 Views   Citations

ABSTRACT

The most significant practical challenge for face recognition is perhaps variability in lighting intensity. In this paper, we developed a face recognition which is insensitive to large variation in illumination. Normalization step including two steps, first we used Histogram truncation as a pre-processing step and then we implemented Homomorphic filter. The main idea is that, achieving illumination invariance causes to simplify feature extraction module and increases recognition rate. Then we utilized Fuzzy Linear Discriminant Analysis (FLDA) in feature extraction stage which showed a good discriminating ability compared to other methods while classification is performed using Feedforward Neural Network (FFNN). The experiments were performed on the ORL (Olivetti Research Laboratory) face image database and the results show the present method outweighs other techniques applied on the same database and reported in literature.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

B. Bozorgtabar, H. Azami and F. Noorian, "Illumination Invariant Face Recognition Using Fuzzy LDA and FFNN," Journal of Signal and Information Processing, Vol. 3 No. 1, 2012, pp. 45-50. doi: 10.4236/jsip.2012.31007.

References

[1] Y. A. Georghiades, P. Belhumeur and D. Kriegman, “From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 6, 2001, pp. 643-660. doi:10.1109/34.927464
[2] K. C. Kwak and W. Pedrycz, “Face Recognition Using a Fuzzy Fisher Classifier,” Pattern Recognition, Vol. 38, No. 10, 2005, pp. 1717-1732. doi:10.1016/j.patcog.2005.01.018
[3] A. M. E. Thammizharasi, “Performance Analysis of Face Recognition by Combining Multiscale Techniques and Homomorphic Filter Using Fuzzy k-Nearest Neighbour Classifier,” IEEE International Conference on Communication Control and Computing Technologies, Ramanathapuram, 7-9 October 2010, pp. 643-650.
[4] N. A. Surobhi and Md. R. Amin, “Employment of Modified Homomorphic Filters in Medical Imaging,” International University Journal of Science and Technology in Daffodil, Vol. 1, No. 1, 2006.
[5] J. M. Keller, M. R. Gray and J. A. Givern, “A Fuzzy K Nearest Neighbor Classifier Algorithm,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 15, No. 4, 1985, pp. 580-585.
[6] W. Yang, H. Yan, J. Wang and J. Yang, “Face Recognition Using Complete Fuzzy LDA,” 19th Conference on International Pattern Recognition, Tampa, 8-11 December 2008, pp. 1-4.
[7] X.-N. Song and Y. Zheng, “A Complete Fuzzy Discriminant Analysis Approach for Face Recognition,” Applied Soft Computing, Vol. 10, No. 1, 2010, pp. 208-214.
[8] M.-Y. Shieh, C.-M. Hsieh and J.-Y. Chen, “PCA and LDA-Based Fuzzy Face Recognition System,” SICE Annual Conference, Taipei, 18-21 August 2010, pp. 1610-1615.
[9] A. Eleyan and H. Demirel, “Face Recognition System Based on PCA and Feedforward Neural Networks,” Proceedings of Computational Intelligence and Bioinspired Systems, Barcelona, June 2005, pp. 935-942. doi:10.1007/11494669_115
[10] S. Lawrence, C. L. Giles, A. C. Tsoi and A. D. Back, “Face Recognition: A Convolutional Neural-Network Approach,” IEEE Transactions on Neural Networks, Vol. 8, No. 1, 1997, pp. 98-113. doi:10.1109/72.554195
[11] Z. J. Yu, “Face Recognition with Eigenface,” Proceeding of the IEEE International Conference on Industrial Technology, Guangzhou, 5-9 December1994, pp. 434-438.
[12] ORL, “The Database of Faces,” 2011. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatase.html

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.