Taxonomy of Filtering Based Illumination Normalization for Face Recognition

Authors

  • Sasan Karamizadeh Advanced Informatics School (AIS), Universiti Teknologi Malaysia, Kuala Lumpur, 54000, Malaysia.
  • Shahidan M Abdullah Advanced Informatics School (AIS), Universiti Teknologi Malaysia, Kuala Lumpur, 54000, Malaysia.
  • Jafar Shayan Advanced Informatics School (AIS), Universiti Teknologi Malaysia, Kuala Lumpur, 54000, Malaysia.
  • Mazdak Zamani Department of Computer Science, Kean University, NJ, USA.
  • Parham Nooralishahi Department of Computer Science and Information Technology, University of Malaya.

Keywords:

Taxonomy, Face Recognition, Strategies, Filters, Illumination,

Abstract

Presently, the difficulty in managing illumination over the face recognition techniques and smooth filters has emerged as one of the biggest challenges. This is due to differences between face images created by illuminations which are always bigger than the inter-person that usually be used for identities’ recognition. No doubt, the use of illumination technique for face recognition is much more popular with a greater number of users in various applications in these days. It is able to make applications that come with face recognition as a non-intrusive biometric feature becoming executable and utilizable. There are tremendous efforts put in developing the illumination and face recognition by which numerous methods had already been introduced. However, further considerations are required such as the deficiencies in comprehending the sub-spaces in illuminations pictures, intractability in face modelling as well as the tedious mechanisms of face surface reflections as far as face recognition and illumination concerned. In this study, few illuminations have been analyzed in order to construct the taxonomy. This covers the background and previous studies in illumination techniques as well the image-based face recognition over illumination. Data was obtained from the year of 1996 through 2014 out of books, journals as well as electronic sources that would share more on the advantageous and disadvantageous, the current technique’s performance as well as future plan.

References

Aly, S., Sagheer, A., Tsuruta, N., and Taniguchi ,R.-i., “Face recognition across illumination,” Artificial Life and Robotics, vol. 12, no. 1-2, pp. 33-37, 2008.

Tan, X., and Triggs, B., “Enhanced local texture feature sets for face recognition under difficult lighting conditions”, IEEE Transactions on Image Processing, vol. 19, no. 6, pp.1635-1650, 2010.

Karamizadeh, S., Abdullah, S.M. and Zamani, M., “An Overview of Holistic Face Recognition,” International Journal of Research in Computer and Communication Technology, vol. 2, no. 9, pp. 738-741, 2013.

Phillips, P.J., Grother, P. and Micheals, R., “Evaluation methods in face recognition”, Springer, 2011.

Zhou, Y., Bao, L, and Lin, Y., “Fast Second-Order Orthogonal Tensor Subspace Analysis for Face Recognition,” Journal of Applied Mathematics, 2014.

Chauhan, T., and Richhariya, V., “Real Time Face Detection with Skin and Feature Based Approach and Reorganization using Genetic Algorithm,” Digital Image Processing, vol. 5, no. 1, pp. 27-31, 2013.

Jobson, D. J., Rahman, Z.-U., and Woodell, G. A., “Properties and performance of a center/surround retinex”, IEEE Transactions on Image Processing, vol. 6, no. 3, pp. 451-462, 1997.

Land, E. H., and McCann, J., “Lightness and retinex theory,” Journal of the Optical Society of America, vol. 61, no. 1, pp. 1-11, 1971.

Rahman, Z.-U., Jobson, D. J., and Woodell, G. A., “Multi-scale retinex for color image enhancement.”, pp. 1003-1006.

Frankle, J. A., and McCann, J. J., “Method and apparatus for lightness imaging”, Google Patents, 1983.

Ciurea, F., and Funt, B., “Tuning retinex parameters”, Journal of Electronic Imaging, vol. 13, no. 1, pp. 58-64, 2004.

Chen, W., Er, M. J., and Wu, S., “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain”, Systems, Man, and Cybernetics, Part B: IEEE Transactions on Cybernetics, vol. 36, no. 2, pp. 458-466, 2006.

Jiang, J., and Feng, G., “Robustness analysis on facial image description in DCT domain,” Electronics Letters, vol. 43, no. 24, pp. 1354-1356, 2007.

Er, M. J., Chen, W., and Wu, S., “High-speed face recognition based on discrete cosine transform and RBF neural networks,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 679-691, 2005.

Sanderson, C., and Paliwal, K. K., “Features for robust face-based identity verification,” Signal Processing, vol. 83, no. 5, pp. 931-940, 2003.

Podilchuk, C. I., and Zhang, X., “Face recognition using DCT-based feature vectors”, Google Patents, 1998.

Hafed, Z. M., and Levine, M. D., “Face recognition using the discrete cosine transform”, International Journal of Computer Vision, vol. 43, no. 3, pp. 167-188, 2001.

Štruc, A. V., “Performance evaluation of photometric normalization techniques for illumination invariant face recognition,” 2010.

Karamizadeh, S., Abdullah, S. M., Zamani, M., and Kherikhah, A., “Pattern Recognition Techniques: Studies on Appropriate Classifications”, Advanced Computer and Communication Engineering Technology, pp. 791-799: Springer, 2015.

Karamizadeh, S., Abdullah , S. M., Halimi, M., Shayan, J., and Rajabi, M. J., “Advantage and Drawback of Support Vector Machine Functionality.”

Karamizadeh, F., "Face Recognition by Implying Illumination

Techniques–A Review Paper." Journal of Science and Engineering,

Vol 6, no. 01, pp. 001-007, 2015.

Karamizadeha, Sasan, Mabdullahb, S., Randjbaranc, E. and Rajabid, M.J, “A Review on Techniques of Illumination in Face Recognition”, Technology 3, no. 02, pp. 79-83, 2015.

Mohammadi, K., Shamshirband, S., Danesh, A.S., Abdullah, M.S. and Zamani, M., “Temperature-based estimation of global solar radiation using soft computing methodologies”, Theoretical and Applied Climatology, pp. 1-12, 2015.

Sadeghian, Alireza, Mahdi Zamani, and Settana M. Abdullah. “A taxonomy of SQL injection attacks.” IEEE International Conference on Informatics and Creative Multimedia (ICICM), 2013.

Karamizadeh, Sasan, Shahidan M. A., Azizah A. M, Zamani, M., and Hooman, A, “An overview of principal component analysis”, Journal of Signal and Information Processing 4, no. 3B, pp. 173, 2013.

Vishwakarma, Virendra P. “Illumination normalization using fuzzy filter in DCT domain for face recognition”, International Journal of Machine Learning and Cybernetics 6, no. 1, 17-34, 2015.

Yuan, Xue, Meng, F. and Wei, X., “Illumination normalization based on homomorphic wavelet filtering for face recognition”, Journal of Information Science and Engineering 29, no. 3, 579-594, 2013.

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Published

2017-04-01

How to Cite

Karamizadeh, S., M Abdullah, S., Shayan, J., Zamani, M., & Nooralishahi, P. (2017). Taxonomy of Filtering Based Illumination Normalization for Face Recognition. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1-5), 135–139. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1851

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