Overview of the Face Recognition Grand Challenge

  • Authors:
  • P. Jonathon Phillips;Patrick J. Flynn;Todd Scruggs;Kevin W. Bowyer;Jin Chang;Kevin Hoffman;Joe Marques;Jaesik Min;William Worek

  • Affiliations:
  • National Institute of Standards and Technology;University of Notre Dame;SAIC;University of Notre Dame;University of Notre Dame;SAIC;The Mitre Corporation;University of Notre Dame;SAIC

  • Venue:
  • CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
  • Year:
  • 2005

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Abstract

Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The Face Recognition Grand Challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.