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Advances in Multimedia
Volume 2017, Article ID 1356385, 11 pages
https://doi.org/10.1155/2017/1356385
Research Article

Efficient Gabor Phase Based Illumination Invariant for Face Recognition

1Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing 210044, China
2School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, China

Correspondence should be addressed to Chunnian Fan; moc.361@tsiungniquuy

Received 13 June 2017; Revised 6 September 2017; Accepted 8 November 2017; Published 27 November 2017

Academic Editor: Haoran Xie

Copyright © 2017 Chunnian Fan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399–458, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. P. J. Phillips, W. T. Scruggs, A. J. O'Toole et al., “FRVT 2006 and ICE 2006 large-scale experimental results,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 831–846, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Adini, Y. Moses, and S. Ullman, “Face recognition: the problem of compensating for changes in illumination direction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 721–732, 1997. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Shan, W. Gao, B. Cao, and D. Zhao, “Illumination normalization for robust face recognition against varying lighting conditions,” in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG '03), pp. 157–164, IEEE, Nice, France, October 2003. View at Publisher · View at Google Scholar
  5. M. Savvides and B. V. Kumar, “Illumination Normalization Using Logarithm Transforms for Face Authentication,” in Audio- and Video-Based Biometric Person Authentication, vol. 2688 of Lecture Notes in Computer Science, pp. 549–556, Springer, Guildford,UK, 2003. View at Publisher · View at Google Scholar
  6. X. Tan and B. Triggs, “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. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. C.-N. Fan and F.-Y. Zhang, “Homomorphic filtering based illumination normalization method for face recognition,” Pattern Recognition Letters, vol. 32, no. 10, pp. 1468–1479, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. P.-H. Lee, S.-W. Wu, and Y.-P. Hung, “Illumination compensation using oriented local histogram equalization and its application to face recognition,” IEEE Transactions on Image Processing, vol. 21, no. 9, pp. 4280–4289, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. P. N. Belhumeur and D. J. Kriegman, “What Is the Set of Images of an Object under All Possible Illumination Conditions?” International Journal of Computer Vision, vol. 28, no. 3, pp. 245–260, 1998. View at Publisher · View at Google Scholar · View at Scopus
  10. 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,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643–660, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. R. Basri and D. W. Jacobs, “Lambertian reflectance and linear subspaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 2, pp. 218–233, 2003. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Horn, Robot Vision, McGraw-Hill, New York, NY, USA, 1986.
  13. D. J. Jobson, Z.-U. Rahman, and G. A. Woodell, “A multiscale retinex for bridging the gap between color images and the human observation of scenes,” IEEE Transactions on Image Processing, vol. 6, no. 7, pp. 965–976, 1997. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Wang, S. Z. Li, and Y. Wang, “Face recognition under varying lighting conditions using self quotient image,” in Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (FGR '04), pp. 819–824, Seoul, Korea, May 2004. View at Scopus
  15. T. Chen, W. Yin, X. S. Zhou, D. Comaniciu, and T. S. Huang, “Total variation models for variable lighting face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1519–1524, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Zhang, B. Fang, Y. Yuan et al., “Multiscale facial structure representation for face recognition under varying illumination,” Pattern Recognition, vol. 42, no. 2, pp. 251–258, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Cheng, Y. Hou, C. Zhao, Z. Li, Y. Hu, and C. Wang, “Robust face recognition based on illumination invariant in nonsubsampled contourlet transform domain,” Neurocomputing, vol. 73, no. 10-12, pp. 2217–2224, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. X. Xie, J. Lai, and W.-S. Zheng, “Extraction of illumination invariant facial features from a single image using nonsubsampled contourlet transform,” Pattern Recognition, vol. 43, no. 12, pp. 4177–4189, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. L.-H. Chen, Y.-H. Yang, C.-S. Chen, and M.-Y. Cheng, “Illumination invariant feature extraction based on natural images statistics Taking face images as an example,” in Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 681–688, Colorado Springs, Colo, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Cao, W. Shen, L. G. Yu, Y. L. Wang, J. Y. Yang, and Z. W. Zhang, “Illumination invariant extraction for face recognition using neighboring wavelet coefficients,” Pattern Recognition, vol. 45, no. 4, pp. 1299–1305, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. T. Song, K. Xiang, and X.-Y. Wang, “Face recognition under varying illumination based on gradientface and local features,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 10, no. 2, pp. 222–228, 2015. View at Publisher · View at Google Scholar · View at Scopus
  22. M. R. Faraji and X. Qi, “Face recognition under varying illuminations using logarithmic fractal dimension-based complete eight local directional patterns,” Neurocomputing, vol. 199, pp. 16–30, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. H. F. Chen, P. N. Belhumeur, and D. W. Jacobs, “In search of illumination invariants,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '00), vol. 1, pp. 254–261, Hilton Head Island, SC, USA, June 2000. View at Scopus
  24. T. Zhang, Y. Y. Tang, B. Fang, Z. Shang, and X. Liu, “Face recognition under varying illumination using gradientfaces,” IEEE Transactions on Image Processing, vol. 18, no. 11, pp. 2599–2606, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  25. X. Chen and J. Zhang, “Illumination robust single sample face recognition using multi-directional orthogonal gradient phase faces,” Neurocomputing, vol. 74, no. 14-15, pp. 2291–2298, 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Zhang, S. Shan, X. Chen, and W. Gao, “Histogram of Gabor phase patterns ({HGPP}): a novel object representation approach for face recognition,” IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 57–68, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  27. A. K. Sao and B. Yegnanarayana, “On the use of phase of the Fourier transform for face recognition under variations in illumination,” Signal, Image and Video Processing, vol. 4, no. 3, pp. 353–358, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. Cheng, C. L. Wang, Z. Y. Li, Y. K. Hou, and C. X. Zhao, “Multiscale principal contour direction for varying lighting face recognition,” IEEE Electronics Letters, vol. 46, no. 10, pp. 680–682, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. J. G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” Journal of the Optical Society of America A: Optics and Image Science, and Vision, vol. 2, no. 7, pp. 1160–1169, 1985. View at Publisher · View at Google Scholar · View at Scopus
  30. L. Wiskott, J.-M. Fellous, N. Krüger, and C. von der Malsburg, “Face recognition by elastic bunch graph matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775–779, 1997. View at Publisher · View at Google Scholar · View at Scopus
  31. C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition,” IEEE Transactions on Image Processing, vol. 11, no. 4, pp. 467–476, 2002. View at Publisher · View at Google Scholar · View at Scopus
  32. W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, “Local gabor binary pattern histogram (LGBPHS): a novel non-statistical model for face representation and recognition,” in Proceedings of the 10th IEEE International Conference on Computer Vision (ICCV '05), pp. 786–791, Beijing, China, October 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. S. Xie, S. Shan, X. Chen, X. Meng, and W. Gao, “Learned local Gabor patterns for face representation and recognition,” Signal Processing, vol. 89, no. 12, pp. 2333–2344, 2009. View at Publisher · View at Google Scholar · View at Scopus
  34. Á. Serrano, I. Martín De Diego, C. Conde, and E. Cabello, “Analysis of variance of Gabor filter banks parameters for optimal face recognition,” Pattern Recognition Letters, vol. 32, no. 15, pp. 1998–2008, 2011. View at Publisher · View at Google Scholar · View at Scopus
  35. J. C. Goswami and A. K. Chan, Fundamentals of Wavelets: Theory, Algorithms, and Applications, Wiley, Hoboken, NJ, USA, 2nd edition, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. K.-C. Lee, J. Ho, and D. J. Kriegman, “Acquiring linear subspaces for face recognition under variable lighting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 684–698, 2005. View at Publisher · View at Google Scholar · View at Scopus
  37. T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression (PIE) database,” in Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–51, Washington, DC, USA, May 2002. View at Publisher · View at Google Scholar
  38. P. J. Phillips, H. Wechsler, J. Huang, and P. J. Rauss, “The FERET database and evaluation procedure for face-recognition algorithms,” Image and Vision Computing, vol. 16, no. 5, pp. 295–306, 1998. View at Publisher · View at Google Scholar · View at Scopus
  39. S. Du and R. Ward, “Wavelet-based illumination normalization for face recognition,” in Proceedings of the IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. 954–957, Genoa, Italy, September 2005. View at Publisher · View at Google Scholar · View at Scopus