Ambient Illumination Variation Removal by Active Near-IR Imaging

  • Xuan Zou
  • Josef Kittler
  • Kieron Messer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

We investigate an active illumination method to overcome the effect of illumination variation in face recognition. Active Near-Infrared (Near-IR) illumination projected by a Light Emitting Diode (LED) light source is used to provide a constant illumination. The difference between two face images captured when the LED light is on and off respectively, is the image of a face under just the LED illumination, and is independent of ambient illumination. In preliminary experiments across different illuminations, across time, and their combinations, significantly better results are achieved in both automatic and semi-automatic face recognition experiments on LED illuminated faces than on face images under ambient illuminations.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bowyer, K.W., Chang, K., Flynn, P.: A survey of approaches to three-dimensional face recognition. In: Proceedings of International Conference on Pattern Recognition (2004)Google Scholar
  2. 2.
    Dowdall, J., Pavlidis, I., Bebis, G.: Face detection in the near-ir spectrum. Image Vis. Comput. 21, 565–578 (2003)CrossRefGoogle Scholar
  3. 3.
    Hamouz, M., Kittler, J., Kamarainen, J.K., Paalanen, P., Kalaviainen, H.: Affine-invariant face detection and localization using gmm-based feature detector and enhanced appearance model. In: Proceedings of the Sixth International Conference on Automatic Face and Gesture Recognition, May 2004, pp. 67–72 (2004)Google Scholar
  4. 4.
    Qiang, J.: 3d face pose estimation and tracking from a monocular camera. Image and Vision Computing 20, 499–511 (2002)CrossRefGoogle Scholar
  5. 5.
    Kong, S.G., Heo, J., Abidi, B., Paik, J., Abidi, M.: Recent advances in visual and infrared face recognition - a review. In: Computer Vision and Image Understanding (2004)Google Scholar
  6. 6.
    Morimoto, C.H., Flickner, M.: Real-time multiple face detection using active illumination. In: Proceedings of the Fourth International Conference on Automatic Face and Gesture Recognition (2000)Google Scholar
  7. 7.
    Short, J., Kittler, J., Messer, K.: A comparison of photometric normalisation algorithm for face verification. In: Proceedings of the Sixth International Conference on Automatic Face and Gesture Recognition, May 2004, pp. 254–259 (2004)Google Scholar
  8. 8.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kaufmann, San Francisco (1999)Google Scholar
  9. 9.
    Ypsilos, I.A., Hilton, A., Rowe, S.: Video-rate capture of dynamic face shape and appearance. In: Proceedings of the Sixth International Conference on Automatic Face and Gesture Recognition, May 2004, pp. 117–122 (2004)Google Scholar
  10. 10.
    Zhao, W., Chellappa, R., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  11. 11.
    Zou, X., Kittler, J., Messer, K.: Face recognition using active near-ir illumination. In: Proceedings of British Machine Vision Conference (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xuan Zou
    • 1
  • Josef Kittler
    • 1
  • Kieron Messer
    • 1
  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyUnited Kingdom

Personalised recommendations