Illumination Invariant Face Recognition using SQI and Weighted LBP Histogram

  • Mohsen Biglari University of Shahrood
  • Faezeh Mirzaei Kashan University
  • Hossein Ebrahimpour-Komleh Kashan University


Face recognition under uneven illumination is still an open problem. One of the main challenges in real-world face recognition systems is illumination variation. In this paper, a novel illumination invariant face recognition approach based on Self Quotient Image (SQI) and weighted Local Binary Pattern (WLBP) histogram has been proposed. In this system, the performance of the system is increased by using different sigma values of SQI for training and testing. Furthermore, using two multi-region uniform LBP operators for feature extraction simultaneously, made the system more robust to illumination variation. This approach gathers information of the image in different local and global levels. The weighted Chi square statistic is used for histogram comparison and NN (1-NN) is used as classifier. The weighted approach emphasizes on the more important regions in the faces. The proposed approach is compared with some new and traditional methods like QI, SQI, QIR, MQI, DMQI, DSFQI, PCA and LDA on Yale face database B and CMU-PIE database. The experimental results show that the proposed method outperforms other tested methods.


[1] M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, vol. 3, pp. 71-86, 1991.
[2] J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, "Face recognition using LDA-based algorithms," Neural Networks, IEEE Transactions on, vol. 14, pp. 195-200, 2003.
[3] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, "Face recognition by independent component analysis," Neural Networks, IEEE Transactions on, vol. 13, pp. 1450-1464, 2002.
[4] Y. Cheng, Z. Jin, and C. Hao, "Illumination Normalization Based on Different Smoothing Filters Quotient Image," in Intelligent Networks and Intelligent Systems (ICINIS), 2010 3rd International Conference on, 2010, pp. 28-31.
[5] S. Z. Li, R. F. Chu, S. C. Liao, and L. Zhang, "Illumination invariant face recognition using near-infrared images," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, pp. 627-639, 2007.
[6] Y. Moses, Y. Adini, and S. Ullman, "Face recognition: The problem of compensating for changes in illumination direction," Computer Vision—ECCV'94, pp. 286-296, 1994.
[7] M. Nishiyama, T. Kozakaya, and O. Yamaguchi, "Illumination Normalization using Quotient Image-based Techniques," Recent Advances in Face Recognition, I-Tech, Vienna, Austria, pp. 97-108, 2008.
[8] J. Wang, L. Wu, and X. He, "A new method of illumination invariant face recognition," in Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference on, 2007, pp. 139-139.
[9] Y. Zhang, J. Tian, X. He, and X. Yang, "MQI based face recognition under uneven illumination," Advances in Biometrics, pp. 290-298, 2007.
[10] L. Zhichao and E. M. Joo, "Face Recognition under Varying Illumination," New Trends in Technologies: Control, Management, Computational Intelligence and Network Systems, 2010.
[11] H. Wang, S. Z. Li, Y. Wang, and J. Zhang, "Self quotient image for face recognition," in Image Processing, 2004. ICIP'04. 2004 International Conference on, 2004, pp. 1397-1400.
[12] A. Shashua and T. Riklin-Raviv, "The quotient image: Class-based re-rendering and recognition with varying illuminations," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 23, pp. 129-139, 2001.
[13] S. Shan, W. Gao, B. Cao, and D. Zhao, "Illumination normalization for robust face recognition against varying lighting conditions," in Analysis and Modeling of Faces and Gestures, 2003. AMFG 2003. IEEE International Workshop on, 2003, pp. 157-164.
[14] X. G. He, J. Tian, L. F. Wu, Y. Y. Zhang, and X. Yang, "Illumination normalization with morphological quotient image," Ruan Jian Xue Bao(Journal of Software), vol. 18, pp. 2318-2325, 2007.
[15] X. Chai, S. Shan, X. Chen, and W. Gao, "Locally linear regression for pose-invariant face recognition," Image Processing, IEEE Transactions on, vol. 16, pp. 1716-1725, 2007.
[16] M. Wai Lee and S. Ranganath, "Pose-invariant face recognition using a 3D deformable model," Pattern Recognition, vol. 36, pp. 1835-1846, 2003.
[17] X. Zhang and Y. Gao, "Face recognition across pose: A review," Pattern Recognition, vol. 42, pp. 2876-2896, 2009.
[18] L. Wiskott, J. M. Fellous, N. Kuiger, and C. von der Malsburg, "Face recognition by elastic bunch graph matching," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 19, pp. 775-779, 1997.
[19] T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, pp. 971-987, 2002.
[20] B. Amberg, R. Knothe, and T. Vetter, "Expression invariant 3D face recognition with a morphable model," in Automatic Face & Gesture Recognition, 2008. FG'08. 8th IEEE International Conference on, 2008, pp. 1-6.
[21] A. Bronstein, M. Bronstein, and R. Kimmel, "Robust expression-invariant face recognition from partially missing data," Computer Vision–ECCV 2006, pp. 396-408, 2006.
[22] H. S. Lee and D. Kim, "Expression-invariant face recognition by facial expression transformations," Pattern recognition letters, vol. 29, pp. 1797-1805, 2008.
[23] I. A. Kakadiaris, G. Passalis, G. Toderici, M. N. Murtuza, Y. Lu, N. Karampatziakis, and T. Theoharis, "Three-dimensional face recognition in the presence of facial expressions: An annotated deformable model approach," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, pp. 640-649, 2007.
[24] N. Alyuz, B. Gokberk, and L. Akarun, "3D Face Recognition under Occlusion using Masked Projection," 2013.
[25] M. De Marsico, M. Nappi, and D. Riccio, "FARO: Face recognition against occlusions and expression variations," Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 40, pp. 121-132, 2010.
[26] N. Alyuz, B. Gokberk, L. Spreeuwers, R. Veldhuis, and L. Akarun, "Robust 3D face recognition in the presence of realistic occlusions," in Biometrics (ICB), 2012 5th IAPR International Conference on, 2012, pp. 111-118.
[27] T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition, vol. 29, pp. 51-59, 1996.
[28] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, "From few to many: Illumination cone models for face recognition under variable lighting and pose," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 23, pp. 643-660, 2001.
[29] X. Zou, J. Kittler, and K. Messer, "Illumination invariant face recognition: A survey," presented at the Biometrics: Theory, Applications, and Systems. First IEEE International Conference on, 2007.
[30] D. Maturana, D. Mery, and Á. Soto, "Face recognition with local binary patterns, spatial pyramid histograms and naive Bayes nearest neighbor classification," 2009, pp. 125-132.
[31] T. Sim, S. Baker, and M. Bsat, "The CMU pose, illumination, and expression (PIE) database," 2002, pp. 46-51.
How to Cite
Biglari, M., Mirzaei, F., & Ebrahimpour-Komleh, H. (2013). Illumination Invariant Face Recognition using SQI and Weighted LBP Histogram. Majlesi Journal of Electrical Engineering, 7(4), 47-54. Retrieved from