Automated scene-specific selection of feature detectors for 3D face reconstruction

  • Authors:

  • Yi Yao;Sreenivas Sukumar;Besma Abidi;David Page;Andreas Koschan;Mongi Abidi

  • Affiliations:

  • Imaging, Robotics, and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics, and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics, and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics, and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics, and Intelligent System Lab, The University of Tennessee, Knoxville, TN;Imaging, Robotics, and Intelligent System Lab, The University of Tennessee, Knoxville, TN

  • Venue:
  • ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
  • Year:
  • 2007

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Abstract

In comparison with 2D face images, 3D face models have the advantage of being illumination and pose invariant, which provides improved capability of handling changing environments in practical surveillance. Feature detection, as the initial process of reconstructing 3D face models from 2D uncalibrated image sequences, plays an important role and directly affects the accuracy and robustness of the resulting reconstruction. In this paper, we propose an automated scene-specific selection algorithm that adaptively chooses an optimal feature detector according to the input image sequence for the purpose of 3D face reconstruction. We compare the performance of various feature detectors in terms of accuracy and robustness of the sparse and dense reconstructions. Our experimental results demonstrate the effectiveness of the proposed selection method from the observation that the chosen feature detector produces 3D reconstructed face models with superior accuracy and robustness to image noise.