: 8-14
Received: July 27, 2020
Accepted: October 25, 2020
Ivan Franko National University of Lviv
Ivan Franko National University of Lviv

Lighting Normalization is an especially important issue in the image recognitions systems since different illumination conditions can significantly change the recognition results, and the lighting normalization allows minimizing negative effects of various illumination conditions. In this paper, we are evaluating the recognition performance of several lighting normalization methods based on the Self-Quotient ImagE(SQI) method introduced by Haitao Wang, Stan Z. Li, Yangsheng Wang, and Jianjun Zhang. For evaluation, we chose the original implementation and the most perspective latest modifications of the original SQI method, including the Gabor Quotient ImagE(GQI) method introduced by Sanun Srisuk and Amnart Petpon in 2008, and the Fast Self-Quotient ImagE(FSQI) method and its modifications proposed by authors in previous works. We are proposing an evaluation framework which uses the Cropped Extended Yale Face Database B, which allows showing the difference of the recognition results for different illumination conditions. Also, we are testing all results using two classifiers: Nearest Neighbor Classifier and Linear Support Vector Classifier. This approach allows us not only to calculate recognition accuracy for each method and select the best method but also show the importance of the proper choice of the classification method, which can have a significant influence on recognition results. We were able to show the significant decreasing of recognition accuracy for un-processed (RAW) images with increasing the angle between the lighting source and the normal to the object. From the other side, our experiments had shown the almost uniform distribution of the recognition accuracy for images processed by lighting normalization methods based on the SQI method. Another showed but expected result represented in this paper is the increasing of the recognition accuracy with the increasing of the filter kernel size. However, the large filter kernel sizes are much more computationally expensive and can produce negative effects on output images. Also, we were shown in our experiments, that the second modification of the FSQI method, called FSQI3, is better almost in all cases for all filter kernel sizes, especially, if we use Linear Support Vector Classifier for classification.

  1. Adini, Y, Moses, Y., & Ullman, S. (1997). Face recognition: the problem of compensating for changes in illumination direction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 721–732.
  2. Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720.
  3. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1996). A Training Algorithm for Optimal Margin Classifier. Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (COLT 92), Association for Computing Machinery, New York, NY, USA, 144–152.
  4. Chen, T., Yin, W., Zhou, X. S., Comaniciu, D., & Huang, T. S. (2005). Illumination normalization for face recognition and uneven background correction using total variation based image models. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR05), 2, 532–539, San Diego, CA, USA.
  5. Cortes, C., & Vapnik, V. (2004). Support-Vector Networks. Machine Learning, 20, 273–297.
  6. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R., & Lin, C.-J. (2008). LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research, 9, 1871–1874.
  7. Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J. (2001). From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 643–660.
  8. Georghiades, A. S., Kriegman, D. J., & Belhumeur, P. N. (1998). Illumination Cones for Recognition under Variable Lighting: Faces. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 98), 52–59.
  9. Gonzalez, R. C., & Woods, R. E. (2001). Digital Image Processing (2nd. ed.). Addison-Wesley Longman Publishing Co. Inc., USA., 793.
  10. Gross, R., & Brajovie, V. (2003). An Image Preprocessing Algorithm for Illumination Invariant Face Recognition. 4th International Conference on Audio and Video Based Biometric Person Authentication (AVBPA), 10–18.
  11. Gryciuk, Yu. I., & Grytsyuk, P. Yu. (2015). Contemporary problems of scientific evaluation of the application software quality. Scientific Bulletin of UNFU, 25(7), 284–294.
  12. Hallinan, P. W. (1994). A low-dimensional representation of human faces for arbitrary lighting conditions. 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 995–999.
  13. Heusch, G., Cardinaux, F., & Marcel, S. (2005). Lighting Normalization Algorithms for Face Verification. IDIAP.
  14. Hrytsiuk, Yuriy, & Bilas, Orest. (2019). Visualization of Software Quality Expert Assessment. IEEE 2019 14th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT 2019), (Vol. 2, pp. 156–160), 17–20 September, 2019.
  15. Jobson, D. J., Rahman, Z., & Woodell, G. A. (1997). A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE transactions on image processing: a publication of the IEEE Signal Processing Society, 6(7), 965–976.
  16. Land, E. H., & McCann, J. J. (1971). Lightness and Retinex Theory. Journal of the Optical Society of America, 61, 1–11.
  17. Lee, K. C., Ho, J., & Kriegman, D. J. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 684–698.
  18. Makwana, R. M. (2010). Illumination invariant face recognition: A survey of passive methods. Procedia Computer Science, 2, 101–110.
  19. Muruganantham, S., & Jebarajan, T. (2011). Exaggerate Self Quotient Image Model for Face Recognition Enlist Subspace Method. International Journal of Computer Science and Information Security (IJCSIS), 9(6), 264–269
  20. Nimeroff, J. S., Simoncelli, E., & Dorsey, J. (1994). Efficient rerendering of naturally illuminated environments. Proceedings of the Fifth Annual Eurographics Symposium on Rendering.
  21. Nishiyama, M., Kozakaya, T., & Yamaguchi, O. (2008). Illumination Normalization using Quotient Image-based Techniques, Recent Advances in Face Recognition KresimirDelac, IntechOpen, 97–108.
  22. Parubochyi, V., & Shuvar, R. (2019). Normalization Modifications for Fast Self-Quotient Image Method. 2019 XIth International Scientific and Practical Conference on Electronics and Information Technologies (ELIT), Lviv, Ukraine, 179–182.
  23. Parubochyi, V., & Shuwar, R. (2018). Fast self-quotient image method for lighting normalization based on modified Gaussian filter kernel. The Imaging Science Journal, 66(8), 471–478.
  24. Pizer, M. S., Amburn, E. P., Austin, J. D., Cromartie, R., Geselowitz, A., Greer, T., Romeny, B. ter H., Zimmerman, J. B., & Zuiderveld, K. (1987). Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3), 355–368.
  25. Reza, A. M. (2004). Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, 38, 35–44.
  26. Riklin-Raviv, T., & Shashua, A. (1999). The quotient image: Class based recognition and synthesis under varying illumination conditions. Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 566–571.
  27. Shashua, A., & Riklin-Raviv, T. (2001). The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 129–139.
  28. Srisuk, S., & Petpon, A. (2008). A Gabor Quotient Image for Face Recognition under Varying Illumination. Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II (ISVC 08), Springer-Verlag, Berlin, Heidelberg, pp. 511–520.
  29. Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1), 71–86.
  30. Wang, H., Li, S. Z., & Wang, Y. (2004). Face recognition under varying lighting conditions using self quotient image. Proceedings of Sixth IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, South Korea, 819–824.
  31. Wang, H., Li, S. Z., & Wang, Y. (2004). Generalized quotient image. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2, 498–505.
  32. Wang, H., Li, S. Z., Wang, Y., & Zhang, J. (2004). Self quotient image for face recognition. 2004 International Conference on Image Processing (ICIP 04), Singapore, 2, 1397–1400.
  33. Xiao-guang, H., Jie, T., Li-fang, W., Yao-yao, Z., & Xin, Y. (2007). Illumination Normalization with Morphological Quotient Image. Journal of Software, 18(9), 2318–2325.
  34. Zou, X., Kittler, J., & Messer, K. (2007). Illumination Invariant Face Recognition: A Survey. First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 1–8.