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G. Bebis et al. (Eds.): ISVC 2007, Part I, LNCS 4841, pp. 476487, 2007. Springer-Verlag Berlin Heidelberg 2007
 

Summary: G. Bebis et al. (Eds.): ISVC 2007, Part I, LNCS 4841, pp. 476487, 2007.
Springer-Verlag Berlin Heidelberg 2007
Automated Scene-Specific Selection of Feature Detectors
for 3D Face Reconstruction
Yi Yao, Sreenivas Sukumar, Besma Abidi, David Page, Andreas Koschan,
and Mongi Abidi
Imaging, Robotics, and Intelligent System Lab
The University of Tennessee, Knoxville, TN, 37996
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 un-
calibrated image sequences, plays an important role and directly affects the
accuracy and robustness of the resulting reconstruction. In this paper, we pro-
pose 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 de-
tectors in terms of accuracy and robustness of the sparse and dense reconstruc-
tions. Our experimental results demonstrate the effectiveness of the proposed
selection method from the observation that the chosen feature detector produces

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee
Koschan, Andreas - Imaging, Robotics, and Intelligent Systems, University of Tennessee

 

Collections: Computer Technologies and Information Sciences; Engineering