Identifying noncooperative subjects at a distance using face images and inferred three-dimensional face models

  • Authors:

  • Gérard Medioni;Jongmoo Choi;Cheng-Hao Kuo;Douglas Fidaleo

  • Affiliations:

  • Institute for Robotics and Intelligent Systems, Viterbi School of Engineering, University of Southern California, Los Angeles, CA;Institute for Robotics and Intelligent Systems, Viterbi School of Engineering, University of Southern California, Los Angeles, CA;Institute for Robotics and Intelligent Systems, Viterbi School of Engineering, University of Southern California, Los Angeles, CA;Big Stage Entertainment Inc., South Pasadena, CA and University of Southern California, Los Angeles, CA

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
  • Year:
  • 2009

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Abstract

We present an approach to identify noncooperative individuals at a distance from a sequence of images, using 3-D face models. Most biometric features (such as fingerprints, hand shape, iris, or retinal scans) require cooperative subjects in close proximity to the biometric system. We process images acquired with an ultrahigh-resolution video camera, infer the location of the subjects' head, use this information to crop the region of interest, build a 3-D face model, and use this 3-D model to perform biometric identification. To build the 3-D model, we use an image sequence, as natural head and body motion provides enough viewpoint variation to perform stereomotion for 3-D face reconstruction. We have conducted experiments on a 2-D and 3-D databases collected in our laboratory. First, we found that metric 3-D face models can be used for recognition by using simple scaling method even though there is no exact scale in the 3-D reconstruction. Second, experiments using a commercial 3-D matching engine suggest the feasibility of the proposed approach for recognition against 3-D galleries at a distance (3, 6, and 9 m). Moreover, we show initial 3-D face modeling results on various factors including head motion, outdoor lighting conditions, and glasses. The evaluation results suggest that video data alone, at a distance of 3 to 9 meters, can provide a 3-D face shape that supports successful face recognition. The performance of 3-D-3-D recognition with the currently generated models does not quite match that of 2-D-2-D. We attribute this to the quality of the inferred models, and this suggests a clear path for future research.