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Handling Illumination Variation: A Challenge for Face Recognition

  • Purvi A. Koringa
  • Suman K. Mitra
  • Vijayan K. Asari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

Though impressive recognition rates have been achieved with various techniques under the controlled face image capturing environment, making recognition more reliable under uncontrolled environment is still a great challenge. Security and surveillance images, captured in open uncontrolled environments, are likely subjected to extreme lighting conditions like underexposed, and overexposed areas that reduce the amount of useful details available in the collected face images. This paper explores two different preprocessing methods and compares the effect of enhancement in recognition results using Orthogonal Neighbourhood preserving Projection (ONPP) and Modified ONPP (MONPP), which are subspace based methods. Note that subspace based face recognition techniques are highly sought after in recent times. Experimental results on preprocessing techniques followed by face recognition using ONPP and MONPP are presented.

Keywords

Illumination variation Dimensionality reduction Face recognition 

Notes

Acknowledgements

The author acknowledges Board of Research in Nuclear Science, BARC, India for the financial support to carry out this research work.

References

  1. 1.
    Y Adini, Y Moses, and S Ullman. Face recognition: The problem of compensating for changes in illumination direction. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19(7):721–732, 1997.CrossRefGoogle Scholar
  2. 2.
    W Chen, M J Er, and S Wu. Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 36(2):458–466, 2006.CrossRefGoogle Scholar
  3. 3.
    S M Pizer, E P Amburn, J D Austin, R Cromartie, A Geselowitz, T Greer, B Romeny, J B Zimmerman, and K Zuiderveld. Adaptive histogram equalization and its variations. Computer vision, graphics, and image processing, 39(3):355–368, 1987.CrossRefGoogle Scholar
  4. 4.
    S Shan, W Gao, B Cao, and D Zhao. Illumination normalization for robust face recognition against varying lighting conditions. In Analysis and Modeling of Faces and Gestures. IEEE International Workshop on, pages 157–164. IEEE, 2003.Google Scholar
  5. 5.
    M Savvides and BVK V Kumar. Illumination normalization using logarithm transforms for face authentication. In Audio-and Video-Based Biometric Person Authentication, pages 549–556. Springer, 2003.Google Scholar
  6. 6.
    E Kokiopoulou and Y Saad. Orthogonal neighborhood preserving projections: A projection-based dimensionality reduction technique. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(12):2143–2156, 2007.CrossRefGoogle Scholar
  7. 7.
    P Koringa, G Shikkenawis, S K Mitra, and SK Parulkar. Modified orthogonal neighborhood preserving projection for face recognition. In Pattern Recognition and Machine Intelligence, pages 225–235. Springer, 2015.Google Scholar
  8. 8.
    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 Trans. Pattern Anal. Mach. Intelligence, 23(6):643–660, 2001.CrossRefGoogle Scholar
  9. 9.
    T Sim, S Baker, and M Bsat. The cmu pose, illumination, and expression database. In Automatic Face and Gesture Recognition. Proceedings. Fifth IEEE International Conference on, pages 46–51. IEEE, 2002.Google Scholar
  10. 10.
    E Krieger, VK Asari, and S Arigela. Color image enhancement of low-resolution images captured in extreme lighting conditions. In SPIE Sensing Technology + Applications, pages 91200Q–91200Q. International Society for Optics and Photonics, 2014.Google Scholar
  11. 11.
    S Arigela and VK Asari. Self-tunable transformation function for enhancement of high contrast color images. Journal of Electronic Imaging, 22(2):023010–023010, 2013.CrossRefGoogle Scholar
  12. 12.
    X Tan and B Triggs. Enhanced local texture feature sets for face recognition under difficult lighting conditions. Image Processing, IEEE Transactions on, 19(6):1635–1650, 2010.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Purvi A. Koringa
    • 1
  • Suman K. Mitra
    • 1
  • Vijayan K. Asari
    • 2
  1. 1.DA-IICTGandhinagarIndia
  2. 2.University of DaytonDaytonUSA

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