Advertisement

Face Recognition in the Thermal Infrared

  • Lawrence B. Wolff
  • Diego A. Socolinsky
  • Christopher K. Eveland
Chapter
Part of the Advances in Pattern Recognition book series (ACVPR)

Summary

Recent research has demonstrated distinct advantages of using thermal infrared imaging for improving face recognition performance. While conventional video cameras sense reflected light, thermal infrared cameras primarily measure emitted radiation from objects such as faces. Visible and thermal infrared image data collections of frontal faces have been on-going at NIST for over two years, producing the most comprehensive face database known to involve thermal infrared imagery. Rigorous experimentation with this database has revealed consistently superior recognition performance of algorithms when applied to thermal infrared, particularly under variable illumination conditions. Physical phenomenology responsible for this observation is analyzed. An end-to-end face recognition system incorporating simultaneous coregistered thermal infrared and visible has been developed and tested indoors with good performance.

Keywords

Face Recognition Linear Discriminant Analysis Focal Plane Array Radiometric Calibration Face Recognition System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Dowdall, J., Pavlidis, I., Bebis, G.: A face detection method based on multib and feature extraction in the near-IR spectrum. In: Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, Kauai, Hawaii (2002)Google Scholar
  2. [2]
    Pavlidis, I., Symosek: The imaging issue in an automatic face/disguise of detection system. In: Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, Hilton Head (2000)Google Scholar
  3. [3]
    Horn, B.: Understanding image intensities. Artificial Intelligence (1977) 1–31Google Scholar
  4. [4]
    Horn, B., Sjoberg, R.: Calculating the reflectance map. Applied Optics 18 (1979) 1770–1779Google Scholar
  5. [5]
    Siegal, R., Howell, J.: Thermal Radiation Heat Transfer. McGraw-Hill, New York (1981)Google Scholar
  6. [6]
    Prokoski, F.: Method for identifying individuals from analysis of elemental shapes derived from biosensor data. In: U.S. Patent 5,163,094, November 10 (1992)Google Scholar
  7. [7]
    DARPA Human Idenification at a Distance (HID) Program, Equinox Corporation, P.W.: Using Visible and Thermal Infrared Imagery for Human Identification. DARPA/AFOSR Contract# F49620-01-C-0008 (2000–2003)Google Scholar
  8. [8]
    Socolinsky, D., Wolff, L., Neuheisel, J., Eveland, C.: Illumination invariant face recognition using thermal IR imagery. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, Hawaii (2001)Google Scholar
  9. [9]
    Socolinsky, D.A., Selinger, A.: A comparative analysis of face recognition performance with visible and thermal infrared imagery. In: Proceedings IAPR International Conference on Pattern Recognition, Quebec, Canada (2002)Google Scholar
  10. [10]
    Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae. Wiley Series in Pure and Applied Optics, John Wiley & Sons (1981)Google Scholar
  11. [11]
    Dereniak, E., Boreman, G.: Infrared Detectors and Systems. John Wiley & Sons (1996)Google Scholar
  12. [12]
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cognitive Neuroscience 3 (1991) 71–86Google Scholar
  13. [13]
    Adini, Y., Moses, Y., Ullman, S.: Face rcognition: The problem of compensating for changes in illumination direction. IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (1997) 721–732CrossRefGoogle Scholar
  14. [14]
    Prokoski, F.: History, current status, and future of infrared identification. In: Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, Hilton Head (2000)Google Scholar
  15. [15]
    Wilder, J., Phillips, P., Jiang, C., Wiener, S.: Comparison of visible and infrared imagery for face recognition. In: Proceedings of 2nd International Conference on Automatic Face & Gesture Recognition, Killington, VT (1996) 182–187Google Scholar
  16. [16]
    P. J. Bickel, K. A. Doksum: Mathematical Statistics. Prentice-Hall, Englewood Cliffs, NJ (1977)Google Scholar
  17. [17]
    Shashua, A.: On photometric issues in 3D visual recognition from a single 2D image. IJCV 21 (1997) 99–122CrossRefGoogle Scholar
  18. [18]
    Shashua, A., Raviv, T.R.: The quotient image: Class-based re-rendering and recognition with varying illuminations. IEEE TPAMI 23 (2001) 129–139Google Scholar
  19. [19]
    Zhao, W., Chellappa, R.: Robust Face Recognition using Symmetric Shape-from-Shading. Technical report, Center for Automation Research, University of Maryland, College Park, MD (1999) Available at http://citeseer.nj.nec.com/zhao99robust.html”.Google Scholar
  20. [20]
    Socolinsky, D.A., Selinger, A., Neuheisel, J.: Face recognition with visible and thermal infrared imagery. Computer Vision and Image Understanding (CVIU) Special Issue on Face Recognition (2003) Submitted.Google Scholar
  21. [21]
    Penev, P., Attick, J.: Local feature analysis: A general statistical theory for object representation. Network: Computation in Neural Systems 7 (1996) 477–500CrossRefGoogle Scholar
  22. [22]
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions PAMI 19 (1997) 711–720Google Scholar
  23. [23]
    Comon, P.: Independent component analysis: a new concept? Signal Processing 36 (1994) 287–314CrossRefzbMATHGoogle Scholar
  24. [24]
    Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET Evaluation Methodology for Face-Recognition Algorithms. Technical Report NISTIR 6264, National Institiute of Standards and Technology (1999)Google Scholar
  25. [25]
    Eveland, C., Socolinsky, D., Wolff, L.: Tracking faces in infrared video. In: Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, Kauai, Hawaii (2002)Google Scholar
  26. [26]
    Prokoski, F.: Method and apparatus for recognizing and classifying individuals based on minutiae. In: U.S. Patent 6,173,068, January 9 (2001)Google Scholar

Copyright information

© Springer-Verlag London Limited 2005

Authors and Affiliations

  • Lawrence B. Wolff
    • 1
  • Diego A. Socolinsky
    • 1
  • Christopher K. Eveland
    • 1
  1. 1.Equinox CorporationNew York

Personalised recommendations