TY - GEN
T1 - Automated scene-specific selection of feature detectors for 3D face reconstruction
AU - Yao, Yi
AU - Sukumar, Sreenivas
AU - Abidi, Besma
AU - Page, David
AU - Koschan, Andreas
AU - Abidi, Mongi
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=38149137362&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-76858-6_47
DO - 10.1007/978-3-540-76858-6_47
M3 - Conference contribution
AN - SCOPUS:38149137362
SN - 9783540768579
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 476
EP - 487
BT - Advances in Visual Computing - Third International Symposium, ISVC 2007, Proceedings
PB - Springer Verlag
T2 - 3rd International Symposium on Visual Computing, ISVC 2007
Y2 - 26 November 2007 through 28 November 2007
ER -