Encyclopedia of Biometrics

2009 Edition
| Editors: Stan Z. Li, Anil Jain

Face Variation

  • Carlos D. Castillo
  • David W. Jacobs
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-73003-5_91

Synonym

Definition

Face variation refers to the way in which the appearance of the face changes due to changes in viewing conditions such as illumination or pose, or due to changes in properties of the face, such as its expression or age.

Introduction

Face recognition is a fundamental problem in biometrics. One of the chief sources of difficulty in face recognition is the large number of variations that can affect the appearance of faces. These include changes in lighting, pose, facial expression, makeup, hair, glasses, facial hair, occlusion by objects that block part of the face from view, aging, and weight gain or loss. Many studies suggest that these variations can significantly reduce the performance of recognition algorithms.

Some face recognition systems aimed at cooperative subjects deal with this problem by attempting to control these sources of variation. This may be appropriate for some applications. In these cases, pose can be controlled by requiring a...

This is a preview of subscription content, log in to check access

Notes

Acknowledgments

The authors have been supported by a fellowship from Apptis, Inc., and by a Honda Research Initiation Grant.

References

  1. 1.
    Phillips, P.J., Scruggs, W.T., O’Toole, A., Flynn, P., Bowyer, K., Schott, C., Sharpe, M.: FRVT 2006 and ICE 2006 large-scale results, National Institute of Standards and Technology Report NISTIR 7408 (2007)Google Scholar
  2. 2.
    Adini, Y., Moses, Y., Ullman, S.: Face recognition: The problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 721–732 (1997)CrossRefGoogle Scholar
  3. 3.
    Basri, R., Jacobs, D.: Illumination modeling for face recognition. In: Li, S., Jain, A. (eds.) The Handbook of Face Recognition, pp. 95–120. Springer, New York (2005)Google Scholar
  4. 4.
    Gross, R., Baker, S., Matthews, I., Kanade, T.: Face recognition across pose and illumination. In: Li, S., Jain, A. (eds.) The Handbook of Face Recognition, pp. 203–228. Springer, New York (2005)Google Scholar
  5. 5.
    Romdhani, S., Ho, J., Vetter, T., Kriegman, D.: Face recognition using 3-D models: Pose and illumination. Proc. IEEE 94(11), 1977–1999 (2006)CrossRefGoogle Scholar
  6. 6.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35(4), 399–458 (2003)CrossRefGoogle Scholar
  7. 7.
    Lades, M., Vorbruggen, J., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42(3), 300–311 (1993)CrossRefGoogle Scholar
  8. 8.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)CrossRefGoogle Scholar
  9. 9.
    Gross, R.: Face databases. In: Li, S., Jain, A. (eds.) The Handbook of Face Recognition, pp. 319–346. Springer, New York (2005)Google Scholar
  10. 10.
    Beymer, D., Poggio, T.: Image representations for visual learning. Science 272, 1905–1909 (1996)CrossRefGoogle Scholar
  11. 11.
    Martinez, A.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell. 24(6), 748–763 (2002)CrossRefGoogle Scholar
  12. 12.
    Ramanathan, R., Chellappa, R.: Face verification across age progression. IEEE Trans. Image Process 15(11), 3349–3361 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Carlos D. Castillo
    • 1
  • David W. Jacobs
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege ParkUSA