June 2006
Volume 6, Issue 6
Free
Vision Sciences Society Annual Meeting Abstract  |   June 2006
Face recognition algorithms surpass humans matching faces in images that vary in illumination
Author Affiliations
  • Alice J. O'Toole
    The University of Texas at Dallas
  • P. Jonathon Phillips
    National Institue of Standards and Technology
  • Fang Jiang
    The University of Texas at Dallas
  • Janet Ayyad
    The University of Texas at Dallas
  • Nils Pénard
    The University of Texas at Dallas
  • Hervé Abdi
    The University of Texas at Dallas
Journal of Vision June 2006, Vol.6, 11. doi:10.1167/6.6.11
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Alice J. O'Toole, P. Jonathon Phillips, Fang Jiang, Janet Ayyad, Nils Pénard, Hervé Abdi; Face recognition algorithms surpass humans matching faces in images that vary in illumination. Journal of Vision 2006;6(6):11. doi: 10.1167/6.6.11.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

We compared the accuracy of seven state-of-the-art face recognition algorithms with human performance on the same task. Humans and algorithms determined whether two face images, taken under different illumination conditions, were pictures of the same person or of different people. The algorithms tested were participants in the Face Recognition Grand Challenge (FRGC) test organized by the National Institutes of Standards and Technology. In that competition, algorithms matched identities in 128 million pairs of face images. For the human experiments, we sampled 120 “easy” and 120 “difficult” face pairs from the FRGC dataset, using similarity scores derived from a control algorithm based on a principal components analysis of the aligned and scaled face images. In three experiments, which varied only in exposure time (Exp. 1 - unlimited; Exp2. - 2s, Exp. 3 - 500ms), humans rated face pairs according to the likelihood that the two people were the same. ROC curves from the humans and the algorithms were compared. Three algorithms outperformed humans at matching face pairs prescreened to be “difficult” (cf., Liu, in press; Xie et al., 2005) and all but one algorithm surpassed humans on the “easy” face pairs. Although illumination variation continues to challenge face recognition algorithms, several current algorithms compete favorably with humans— even if they appear to perform poorly in absolute terms.

O'Toole, A. J. Phillips, P. J. Jiang, F. Ayyad, J. Pénard, N. Abdi, H. (2006). Face recognition algorithms surpass humans matching faces in images that vary in illumination [Abstract]. Journal of Vision, 6(6):11, 11a, http://journalofvision.org/6/6/11/, doi:10.1167/6.6.11. [CrossRef]
Footnotes
 Work supported by TSWG funding.
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×