Difference-Based Local Gradient Patterns for Image Representation

  • Shimaa Saad
  • Alaa SagheerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


This paper aims to examine the impact of pixel differences on local gradient patterns (LGP) for representing facial images. Two difference-based descriptors are proposed, namely, the angular difference LGP (AD-LGP) and the radial difference LGP (RD-LGP) descriptors. For evaluation purpose, two experiments are conducted. The first is face/non face classification using samples from CMU-PIE and CBCL databases. The second is face identification under illumination variations using the extended Yale face database B and the CMU-PIE face database. The experimental results show that both descriptors demonstrate, generally, a higher capability in discriminating face patterns from non-face patterns than the standard LGP. However, in face identification, the AD-LGP descriptor shows robustness against illumination variations, while the performance of the RD-LGP descriptor degrades with hard illuminations. Furthermore, we enhance the RD-LGP descriptor using the Average-Before-Quantization (ABQ) approach in order to increase its robustness toward illumination changes.


Local gradient patterns Face identification Face non-face classification 


  1. 1.
    Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2d and 3d face recognition: A survey. Pattern Recognition Letters 28(14), 1885–1906 (2007)CrossRefGoogle Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with local binary patterns. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  3. 3.
    Delac, K., Grgic, M., Bartlett, M.S.: Recent advances in face recognition. Tech Publication, Crosia (2008)CrossRefGoogle Scholar
  4. 4.
    Fischer, P., Brox, T.: Image descriptors based on curvature histograms. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 239–249. Springer, Heidelberg (2014) Google Scholar
  5. 5.
    Hadid, A., Pietikainen, M., Ahonen, T.: A discriminative feature space for detecting and recognizing faces. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. II-797. IEEE (2004)Google Scholar
  6. 6.
    Jain, A.K., Li, S.Z.: Handbook of face recognition, vol. 1. Springer (2005)Google Scholar
  7. 7.
    Jun, B., Kim, D.: Robust face detection using local gradient patterns and evidence accumulation. Pattern Recognition 45(9), 3304–3316 (2012)CrossRefGoogle Scholar
  8. 8.
    Lee, K.C., Ho, J., Kriegman, D.J.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 684–698 (2005)CrossRefGoogle Scholar
  9. 9.
    Liu, L., Long, Y., Fieguth, P., Lao, S., Zhao, G.: Brint: Binary rotation invariant and noise tolerant texture classification. IEEE Transactions on Image Processing 23 (2013)Google Scholar
  10. 10.
    Liu, L., Zhao, L., Long, Y., Kuang, G., Fieguth, P.: Extended local binary patterns for texture classification. Image and Vision Computing 30(2), 86–99 (2012)CrossRefGoogle Scholar
  11. 11.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)CrossRefGoogle Scholar
  12. 12.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer vision using local binary patterns, vol. 40. Springer Science & Business Media (2011)Google Scholar
  13. 13.
    Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing 27(6), 803–816 (2009)CrossRefGoogle Scholar
  14. 14.
    Sim, T., Baker, S., Bsat, M.: The cmu pose, illumination, and expression (pie) database. In: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002, pp. 46–51. IEEE (2002)Google Scholar
  15. 15.
    Cbcl face database, mit center for biological and computation learning.
  16. 16.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Philip, S.Y., et al.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 1–37 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science, College of Computer Science and Information TechnologyKing Faisal UniversityHofufKingdome of Saudi Arabia
  2. 2.Center for Artificial Intelligence and Robotics, Faculty of ScienceAswan UniversityAswanEgypt

Personalised recommendations