Seeing People in the Dark: Face Recognition in Infrared Images

  • Gil Friedrich
  • Yehezkel Yeshurun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2525)


An IR image of the human face presents its unique heat-signature and can be used for recognition. The characteristics of IR images maintain advantages over visible light images, and can be used to improve algorithms of human face recognition in several aspects. IR images are obviously invariant under extreme lighting conditions (including complete darkness). The main findings of this research are that IR face images are less effected by changes of pose or facial expression and enable a simple method for detection of facial features. In this paper we explore several aspects of face recognition in IR images. First, we compare the effect of varying environment conditions over IR and visible light images through a case study. Finally, we propose a method for automatic face recognition in IR images, through which we use a preprocessing algorithm for detecting facial elements, and show the applicability of commonly used face recognition methods in the visible light domain.


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  1. 1.
    M. Turk and A. Pentland (1991). Eigenfaces for recognition, J. Cog. Neuroscience, vol. 3, no. 1, pp. 71–86.CrossRefGoogle Scholar
  2. 2.
    M. Kirby and L. Sirovich (1990). Application of the karhunen-loeve procedure for the characterization of human faces, IEEE Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 103–108.CrossRefGoogle Scholar
  3. 3.
    R. Chellappa, C. Wilson, and S. Sirohev (1995). Human and machine recognition of faces: A survey, in Proceedings of IEEE, May 1995, vol. 83, pp. 705–740.Google Scholar
  4. 4.
    P. Phillips, H. Wechsler, J. Huang, and P. Rauss (1998). The FERET database and evaluation procedure for face recognition algorithms, Image and Vision Computing, vol. 16, no. 5, pp. 295–306.CrossRefGoogle Scholar
  5. 5.
    L. Wiskott, J-M. Fellous, N. Kruger, and C. von der Malsburg (1997). Face recognition by elastic bunch graph matching, IEEE Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775–779.CrossRefGoogle Scholar
  6. 6.
    K. Etemad and R. Chellappa (1997). Discriminant analysis for recognition of human face images, Journal of the Optical Society of America, vol. 14, pp. 1724–1733.CrossRefGoogle Scholar
  7. 7.
    B. Moghaddam and A. Pentland (1997). Probabalistic visual recognition for object recognition, IEEE Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696–710.CrossRefGoogle Scholar
  8. 8.
    M. Weiser (1991). The computer for the 21st century, Scientific American, vol. 265, no. 3, pp. 66–76.CrossRefGoogle Scholar
  9. 9.
    R. Cutler (1996). Face recognition using infrared images and eigenfaces.
  10. 10.
    J. Wilder, P.J. Phillips, C. Jiang, and S. Wiener. Comparison of Visible and Infra-Red Imagery for Face Recognition, Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition, Killington, VT, pp.182–187, October 1996Google Scholar
  11. 11.
    F. Prokoski. History, Current Status, and Future of Infrared Identification, IEEE Workshop on Computer Vision behind the Visible Spectrum: Methods and Applications (CVBVS 2000), pp 5–14.Google Scholar
  12. 12.
    D. Reisfeld and Y. Yeshurun. Preprocessing of Face Images: Detection of Features and Pose Normalization, Computer Vision and Image Understanding, Vol. 71 No. 3 pp 413–430, Sep 1998.CrossRefGoogle Scholar
  13. 13.
    M. Irani and P. Anandan. Robust Multi-Sensor Image Alignment, International Conference on Computer Vision, Mumbai, January 1998.Google Scholar
  14. 14.
    N. Intrator, D. Reisfeld, Y. Yeshurun. Face Recognition using a Hybrid Supervised/ Unsupervised Neural Network, Pattern Recognition Letters 17:67–76, 1996.CrossRefGoogle Scholar
  15. 15.
    Zhang, Y.J. Evaluation and Comparison of Different Segmentation Algorithms, PRL(18), No. 10, October 1997, pp. 963–974.CrossRefGoogle Scholar
  16. 16.
    Zhou, Y.T, Venkateswar, V. and Chellappa R. Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model, PAMI(11), No. 1, January 1989, pp. 84–95.CrossRefGoogle Scholar
  17. 17.
    Harley, R.L., Wang, C.Y, Kitchen, L. and Rosenfeld, A. Segmentation of FLIR Images: A Comparative Study, SMC(12), No. 4, July/August 1982, pp. 553–566, or DARPA82 (323-341).Google Scholar
  18. 18.
    John F. Haddon and James. F. Boyce. Image Segmentation by Unifying Region and Boundary Information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(10), pp. 929–948, October, 1990.CrossRefGoogle Scholar
  19. 19.
    J. Dowdall, I. Pavlidis, and G. Bebis. A Face Detetcion Method Based on Multi-Band Feature Extraction in Near IR Spectrum, IEEE Workshop on Computer Vision Beyond the Visible Spectrum. Kauai, December 2001.Google Scholar
  20. 20.
    Socolinsky, D., Wolff, L., Neuheisel, J., and Eveland, C. Illumination Invariant Face Recognition Using Thermal Infrared Imagery, Computer Vision and Pattern Recognition, Kauai, December 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Gil Friedrich
    • 1
  • Yehezkel Yeshurun
    • 1
  1. 1.School of Computer ScienceTel-Aviv UniversityIsrael

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