Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference

  • Conrad Sanderson
  • Brian C. Lovell
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

We propose a scalable face matching algorithm capable of dealing with faces subject to several concurrent and uncontrolled factors, such as variations in pose, expression, illumination, resolution, as well as scale and misalignment problems. Each face is described in terms of multi-region probabilistic histograms of visual words, followed by a normalised distance calculation between the histograms of two faces. We also propose a fast histogram approximation method which dramatically reduces the computational burden with minimal impact on discrimination performance. Experiments on the “Labeled Faces in the Wild” dataset (unconstrained environments) as well as FERET (controlled variations) show that the proposed algorithm obtains performance on par with a more complex method and displays a clear advantage over predecessor systems. Furthermore, the use of multiple regions (as opposed to a single overall region) improves accuracy in most cases, especially when dealing with illumination changes and very low resolution images. The experiments also show that normalised distances can noticeably improve robustness by partially counteracting the effects of image variations.

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References

  1. 1.
    Rodriguez, Y., Cardinaux, F., Bengio, S., Mariethoz, J.: Measuring the performance of face localization systems. Image and Vision Comput. 24, 882–893 (2006)Google Scholar
  2. 2.
    Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)Google Scholar
  3. 3.
    Sanderson, C., Shan, T., Lovell, B.C.: Towards pose-invariant 2D face classification for surveillance. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 276–289. Springer, Heidelberg (2007)Google Scholar
  4. 4.
    Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report 07-49 (October 2007)Google Scholar
  6. 6.
    Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)Google Scholar
  7. 7.
    Gonzales, R., Woods, R.: Digital Image Processing, 3rd edn. Prentice-Hall, Englewood Cliffs (2007)Google Scholar
  8. 8.
    Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)Google Scholar
  9. 9.
    Kadir, T., Brady, M.: Saliency, scale and image description. International Journal of Computer Vision 45(2), 83–105 (2001)Google Scholar
  10. 10.
    Sanderson, C.: Biometric Person Recognition — Face, Speech and Fusion. VDM Verlag (2008)Google Scholar
  11. 11.
    Lucey, S., Chen, T.: A GMM parts based face representation for improved verification through relevance adaptation. In: Proc. Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 855–861 (2004)Google Scholar
  12. 12.
    Nefian, A., Hayes, M.: Face recognition using an embedded HMM. In: Proc. Audio Video-based Biometric Person Authentication (AVBPA), pp. 19–24 (1999)Google Scholar
  13. 13.
    Cardinaux, F., Sanderson, C., Bengio, S.: User authentication via adapted statistical models of face images. IEEE Trans. Signal Processing 54(1), 361–373 (2006)Google Scholar
  14. 14.
    Martínez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Analysis and Machine Intelligence 24(6), 748–763 (2002)Google Scholar
  15. 15.
    Nowak, E., Jurie, F.: Learning visual similarity measures for comparing never seen objects. In: Proc. Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)Google Scholar
  16. 16.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Conrad Sanderson
    • 1
    • 2
  • Brian C. Lovell
    • 1
    • 2
  1. 1.NICTASt LuciaAustralia
  2. 2.School of ITEEThe University of QueenslandAustralia

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