Robust Face Recognition System Using a Reliability Feedback

  • Shotaro Miwa
  • Shintaro Watanabe
  • Makito Seki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)


In the real world there are a variety of lighting conditions, and there exist many directional lights as well as ambient lights. These directional lights cause partial dark and bright regions on faces. Even if auto exposure mode of cameras is used, those uneven pixel intensities are left, and in some cases saturated pixels and black pixels appear. In this paper we propose robust face recognition system using a reliability feedback. The system evaluates the reliability of the input face image using prior distributions of each recognition feature, and if the reliability of the image is not enough for face recognition, it capture multiple images by changing exposure parameters of cameras based on the analysis of saturated pixels and black pixels. As a result the system can cumulates similarity scores of enough amounts of reliable recognition features from multiple face images. By evaluating the system in an office environment, we can achieve three times better EER than the system only with auto exposure control.


Face Recognition Prior Probability Probabilistic Model 


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  1. 1.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face Recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003)Google Scholar
  2. 2.
    Wiskott, L., Fellous, J.-M., Kruger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Trans. PAMI 19(7), 775–779 (1997)CrossRefGoogle Scholar
  3. 3.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Trans. PAMI 28(12) (2006)Google Scholar
  4. 4.
    Moghaddam, B.: Principal Manifolds and Probabilistic Subspaces for Visual Recognition. IEEE Trans. PAMI 24(6), 780–788 (2002)CrossRefGoogle Scholar
  5. 5.
    Jones, M., Violal, P.: Face Recognition Using Boosted Local Features. In: MERL Tech. Report, TR2003-25 (2003)Google Scholar
  6. 6.
    Miwa, S., Kage, H., Hirai, T., Sumi, K.: Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model. IEEJ Trans. on Electronics, Information and Systems 131(12), 2165–2171 (2011)CrossRefGoogle Scholar
  7. 7.
    Kumar, R., Jones, M., Marks, T.K.: Morphable Reflectance Fields for Enhancing Face Recognition. In: IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2606–2613 (2010)Google Scholar
  8. 8.
    Schapire, R., Singer, Y.: Improving boosting algorithms using confidence-rated predictions (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shotaro Miwa
    • 1
  • Shintaro Watanabe
    • 1
  • Makito Seki
    • 1
  1. 1.Advanced Technology R&D Center, Mitsubishi Electric Corp.Amagasaki CityJapan

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