KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Face Recognition based on Weber Symmetrical Local Graph Structure

  • Yang, Jucheng (College of Computer Science and Information Engineering Tianjin University of Science and Technology) ;
  • Zhang, Lingchao (College of Computer Science and Information Engineering Tianjin University of Science and Technology) ;
  • Wang, Yuan (College of Computer Science and Information Engineering Tianjin University of Science and Technology) ;
  • Zhao, Tingting (College of Computer Science and Information Engineering Tianjin University of Science and Technology) ;
  • Sun, Wenhui (College of Computer Science and Information Engineering Tianjin University of Science and Technology) ;
  • Park, Dong Sun (Department of Electronic and Information Engineering, Chonbuk National University)
  • Received : 2017.07.25
  • Accepted : 2017.11.29
  • Published : 2018.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Weber Local Descriptor (WLD) is a stable and effective feature extraction algorithm, which is based on Weber's Law. It calculates the differential excitation information and direction information, and then integrates them to get the feature information of the image. However, WLD only considers the center pixel and its contrast with its surrounding pixels when calculating the differential excitation information. As a result, the illumination variation is relatively sensitive, and the selection of the neighbor area is rather small. This may make the whole information is divided into small pieces, thus, it is difficult to be recognized. In order to overcome this problem, this paper proposes Weber Symmetrical Local Graph Structure (WSLGS), which constructs the graph structure based on the $5{\times}5$ neighborhood. Then the information obtained is regarded as the differential excitation information. Finally, we demonstrate the effectiveness of our proposed method on the database of ORL, JAFFE and our own built database, high-definition infrared faces. The experimental results show that WSLGS provides higher recognition rate and shorter image processing time compared with traditional algorithms.

Keywords

References

  1. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face recognition: A literature survey," ACM computing surveys (CSUR), vol. 35, no. 4, pp. 399-458, 2003. https://doi.org/10.1145/954339.954342
  2. H. Yu and J. Yang, "A direct LDA algorithm for high-dimensional data-with application to face recognition," Pattern recognition, vol. 34, no. 10, pp. 2067-2070, 2001. https://doi.org/10.1016/S0031-3203(00)00162-X
  3. Ojala, Timo, Matti Pietikainen, and David Harwood. "Performance evaluation of texture measures with classification based on Kullback discrimination of distributions," in Proc. of Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on. Vol. 1. IEEE, vol. 1, pp. 582-585, 1994.
  4. M. Heikkila, M. Pietikainen, and C. Schmid, "Description of interest regions with local binary patterns," Pattern recognition, vol. 42, no. 3, pp. 425-436, 2009. https://doi.org/10.1016/j.patcog.2008.08.014
  5. X. Yang, X. Gao, and Q. Tian, "Polar embedding for aurora image retrieval," IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3332-3344, 2015. https://doi.org/10.1109/TIP.2015.2442913
  6. J. Chen et al., "WLD: A robust local image descriptor," IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 9, pp. 1705-1720, 2010. https://doi.org/10.1109/TPAMI.2009.155
  7. Abusham, Eimad EA, and Housam K. Bashir. "Face recognition using local graph structure (LGS)," in Proc. of International Conference on Human-Computer Interaction. Springer, Berlin, Heidelberg, pp. 169-175, 2011.
  8. M. F. A. Abdullah, M. S. Sayeed, K. S. Muthu, H. K. Bashier, A. Azman, and S. Z. Ibrahim, "Face recognition with symmetric local graph structure (slgs)," Expert Systems with Applications, vol. 41, no. 14, pp. 6131-6137, 2014. https://doi.org/10.1016/j.eswa.2014.04.006
  9. Y. Luo, T. Zhang, and Y. Zhang, "A novel fusion method of PCA and LDP for facial expression feature extraction," Optik-International Journal for Light and Electron Optics, vol. 127, no. 2, pp. 718-721, 2016. https://doi.org/10.1016/j.ijleo.2015.10.147
  10. M. Turk and A. Pentland, "Eigenfaces for recognition," Journal of cognitive neuroscience, vol. 3, no. 1, pp. 71-86, 1991. https://doi.org/10.1162/jocn.1991.3.1.71
  11. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: theory and applications," Neurocomputing, vol. 70, no. 1, pp. 489-501, 2006. https://doi.org/10.1016/j.neucom.2005.12.126
  12. W. Zong and G.-B. Huang, "Face recognition based on extreme learning machine," Neurocomputing, vol. 74, no. 16, pp. 2541-2551, 2011. https://doi.org/10.1016/j.neucom.2010.12.041
  13. Chen, Jie, et al. "A robust descriptor based on weber's law," in Proc. of Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on. IEEE, pp. 1-7, 2008.
  14. S. Li, D. Gong, and Y. Yuan, "Face recognition using Weber local descriptors," Neurocomputing, vol. 122, pp. 272-283, 2013. https://doi.org/10.1016/j.neucom.2013.05.038
  15. B. Wang, W. Li, W. Yang, and Q. Liao, "Illumination normalization based on weber's law with application to face recognition," IEEE Signal Processing Letters, vol. 18, no. 8, pp. 462-465, 2011. https://doi.org/10.1109/LSP.2011.2158998
  16. F. S. Samaria and A. C. Harter, "Parameterisation of a stochastic model for human face identification," in Proc. of Proceedings of the Second IEEE Workshop on, presented at the Applications of Computer Vision, 1994., pp. 138-142, 1994.
  17. M. J. Lyons, J. Budynek, and S. Akamatsu, "Automatic classification of single facial images," IEEE transactions on pattern analysis and machine intelligence, vol. 21, no. 12, pp. 1357-1362, 1999. https://doi.org/10.1109/34.817413