Comparison of Visible, Thermal Infra-Red and Range Images for Face Recognition

  • Ajmal Mian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

Existing literature compares various biometric modalities of the face for human identification. The common criterion used for comparison is the recognition rate of different face modalities using the same recognition algorithms. Such comparisons are not completely unbiased as the same recognition algorithm or features may not be suitable for every modality of the face. Moreover, an important aspect which is overlooked in these comparisons is the amount of variation present in each modality which will ultimately effect the database size each modality can handle. This paper presents such a comparison between the most common biometric modalities of the face namely visible, thermal infra-red and range images. Experiments are performed on the Equinox and the FRGC databases with results indicating that visible images capture more interpersonal variations of the human face compared to thermal IR and range images. We conclude that under controlled conditions, visible face images have a greater potential of accommodating large databases compared to long-wave IR and range images.

Keywords

Face Recognition Visible Image Range Image Quadratic Discriminant Analysis Interpersonal Variation 
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.

References

  1. 1.
    Turk, M., Pentland, A.: Eigenfaces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  2. 2.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Survey, 399–458 (2003)Google Scholar
  3. 3.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 711–720 (1997)CrossRefGoogle Scholar
  4. 4.
    Srivastava, S., Gupta, M., Frigyik, B.: Bayesian Quadratic Discriminant Analysis. The Journal of Machine Learning Research 8(3), 1277–1305 (2007)MathSciNetMATHGoogle Scholar
  5. 5.
    Lu, J., Plataniotis, K., Venetsanopoulos, A.: Regularized discriminant analysis for the small sample size problem in face recognition. Pattern Recognition Letters 24(16), 3079–3087 (2003)CrossRefGoogle Scholar
  6. 6.
    Bowyer, K.W., Chang, K., Flynn, P.: A Survey Of Approaches and Challenges in 3D and Multi-modal 3D + 2D Face Recognition. Computer Vision and Image Understanding 101(1), 1–15 (2006)CrossRefGoogle Scholar
  7. 7.
    Blanz, V., Vetter, T.: Face Recognition Based on Fitting a 3D Morphable Model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1063–1074 (2003)CrossRefGoogle Scholar
  8. 8.
    Bronstein, A.M., Bronstein, M.M., Kimmel, R.: Three-dimensional face recognition. International Journal of Computer Vision 64(1), 5–30 (2005)CrossRefGoogle Scholar
  9. 9.
    Li, S.Z., Chu, R., Liao, S., Zhang, L.: Illumination invariant face recognition using near-infrared images. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 627–639 (2007)CrossRefGoogle Scholar
  10. 10.
    Jain, A., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Kluwer Academic Publishers, Dordrecht (1999)CrossRefGoogle Scholar
  11. 11.
    Chen, X., Flynn, P., Bowyer, K.: Ir and visible light face recognition. Computer Vision and Image Understanding 99(3), 332–358 (2005)CrossRefGoogle Scholar
  12. 12.
    Socolinsky, D., Wolff, L., Neuheisel, J., Eveland, C.: Illumination invariant face recognition using thermal infrared imagery. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 527–534 (2001)Google Scholar
  13. 13.
    Socolinsky, D., Selinger, A.: A comparative analysis of face recognition performance with visible and thermal infrared imagery. In: International Conference on Pattern Recognition, vol. 4, pp. 217–222 (2002)Google Scholar
  14. 14.
    Penev, P., Attick, J.: Local Feature Analysis: A general statistical theory for object representation. Network: Computation in Neural Systems 7(3), 477–500 (1996)CrossRefMATHGoogle Scholar
  15. 15.
    Bartlett, M.S., Lades, H.M., Sejnowski, T.: Independent Component Representation for Face Recognition. In: SPIE, pp. 528–539 (1998)Google Scholar
  16. 16.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Chang, K., Bowyer, K., Flynn, P.: Multi-Modal 2D and 3D Biometrics for Face Recognition. In: IEEE Analysis and Modeling of Faces and Gestures, pp. 187–194 (2003)Google Scholar
  18. 18.
    Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: IEEE Computer Vision and Pattern Recognition, pp. 947–954 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ajmal Mian
    • 1
  1. 1.School of Computer Science and Software EngineeringThe University of Western AustraliaCrawleyAustralia

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