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On the Effects of Illumination Normalization with LBP-Based Watchlist Screening

  • Ibtihel Amara
  • Eric GrangerEmail author
  • Abdenour Hadid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8926)

Abstract

Still-to-video face recognition (FR) is an important function in several video surveillance applications like watchlist screening, where faces captured over a network of video cameras are matched against reference stills belonging to target individuals. Screening of faces against a watchlist is a challenging problem due to variations in capturing conditions (e.g., pose and illumination), to camera inter-operability, and to the limited number of reference stills. In holistic approaches to FR, Local Binary Pattern (LBP) descriptors are often considered to represent facial captures and reference stills. Despite their efficiency, LBP descriptors are known as being sensitive to illumination changes. In this paper, the performance of still-to-video FR is compared when different passive illumination normalization techniques are applied prior to LBP feature extraction. This study focuses on representative retinex, self-quotient, diffusion, filtering, means de-noising, retina, wavelet and frequency-based techniques that are suitable for fast and accurate face screening. Experimental results obtained with videos from the Chokepoint dataset indicate that, although Multi-Scale Weberfaces and Tan and Triggs techniques tend to outperform others, the benefits of these techniques varies considerably according to the individual and illumination conditions. Results suggest that a combination of these techniques should be selected dynamically based on changing capture conditions.

Keywords

Illumination normalization Local binary patterns Face screening Still-to-video face recognition Video surveillance 

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. TPAMI 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  2. 2.
    Barr, J.R., Bowyer, K.W., Flynn, P.J., Biswas, S.: Face recognition from video: A review. International Journal of Pattern Recognition and Artificial Intelligence 26(05) (2012)Google Scholar
  3. 3.
    Chellappa, R., Sinha, P., Phillips, P.J.: Face recognition by computers and humans. Computer 43(2), 46–55 (2010)CrossRefGoogle Scholar
  4. 4.
    Dewan, M., Granger, E., Roli, F., Sabourin, R., Marcialis, G.L.: A comparison of adaptive appearance methods for tracking faces in video surveillance. In: The 5th International Conference on Imaging for Crime Detection and Prevention, December 16–17 (2013)Google Scholar
  5. 5.
    Ekenel, H.K., Stallkamp, J., Stiefelhagen, R.: A video-based door monitoring system using local appearance-based face models. CVIU 114(5), 596–608 (2010)Google Scholar
  6. 6.
    He, C., Ahonen, T., Pietikainen, M.: A bayesian local binary pattern texture descriptor. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4 (December 2008)Google Scholar
  7. 7.
    Kan, M., Shan, S., Su, Y., Xu, D., Chen, X.: Adaptive discriminant learning for face recognition. Pattern Recognition 46(9), 2497–2509 (2013)CrossRefGoogle Scholar
  8. 8.
    Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination Invariant Face Recognition Using Near-Infrared Images. IEEE T. PAMI 29(4), 627–639 (2007)CrossRefGoogle Scholar
  9. 9.
    Liao, S., Zhao, G., Kellokumpu, V., Pietikainen, M., Li, S.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1301–1306 (June 2010)Google Scholar
  10. 10.
    Marcel, S., Rodriguez, Y., Heusch, G.: On the recent use of local binary patterns for face authentication. International Journal of Image and Video Processing, Special Issue on Facial Image Processing, 469–481 (2007)Google Scholar
  11. 11.
    Matta, F., Dugelay, J.L.: Person recognition using facial video information: a state of the art. Journal of Visual Languages and Computing 20(3), 180–187 (2009)CrossRefGoogle Scholar
  12. 12.
    Nasrollahi, K., Moeslund, T.B.: Face quality assessment system in video sequences. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BIOID 2008. LNCS, vol. 5372, pp. 10–18. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  13. 13.
    Nikan, S., Ahmadi, M.: Human face recognition under occlusion using lbp and entropy weighted voting. In: ICPR, pp. 1699–1702. IEEE (2012)Google Scholar
  14. 14.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24(7), 971–987 (2002)CrossRefGoogle Scholar
  15. 15.
    Pagano, C., Granger, E., Sabourin, R., Gorodnichy, D.O.: Detector ensembles for face recognition in video surveillance. In: IJCNN, pp. 1–8. IEEE (2012)Google Scholar
  16. 16.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns. Springer (2011)Google Scholar
  17. 17.
    Sharma, A., Kaushik, V.D., Gupta, P.: Illumination invariant face recognition. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2014. LNCS, vol. 8588, pp. 308–319. Springer, Heidelberg (2014) CrossRefGoogle Scholar
  18. 18.
    Struc, V., Pavesic, N.: Performance evaluation of photometric normalization techniques for illumination invariant face recognition. In: Zhang, Y. (ed.) Advances in Face Image Analysis: Techniques and Technologies. IGI Global, Hershey (2011)Google Scholar
  19. 19.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  20. 20.
    Topcu, B., Erdogan, H.: Decision fusion for patch-based face recognition. In: ICPR, pp. 1348–1351. IEEE (2010)Google Scholar
  21. 21.
    De-la Torre, M., Granger, E., Sabourin, R., Gorodnichy, D.O.: Partially-supervised learning from facial trajectories for face recognition in video surveillance. Information Fusion. Doi:  10.1016/j.inffus.2014.05.006 (2014, in Press)
  22. 22.
    Štruc, V., Pavešić, N.: Gabor-based kernel partial-least-squares discrimination features for face recognition. Informatica (Vilnius) 20(1), 115–138 (2009)zbMATHGoogle Scholar
  23. 23.
    Štruc, V., Pavešić, N.: IGI Global (2011)Google Scholar
  24. 24.
    Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.C.: Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition. In: IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 81–88. IEEE (June 2011)Google Scholar
  25. 25.
    Zou, X., Kittler, J., Messer, K.: Illumination invariant face recognition: A survey. In: First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007, pp. 1–8 (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.École de Technologie SupérieureUniversité du QuébecMontrealCanada
  2. 2.Center for Machine Vision ResearchUniversity of OuluOuluFinland

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