TY - GEN
T1 - Benchmarking asymmetric 3D-2D face recognition systems
AU - Zhao, Xi
AU - Zhang, Wuming
AU - Evangelopoulos, Georgios
AU - Huang, Di
AU - Shah, Shishir K.
AU - Wang, Yunhong
AU - Kakadiaris, Ioannis A.
AU - Chen, Liming
PY - 2013
Y1 - 2013
N2 - Asymmetric 3D-2D face recognition (FR) aims to recognize individuals from 2D face images using textured 3D face models in the gallery (or vice versa). This new FR scenario has the potential to be readily deployable in field applications while still keeping the advantages of 3D FR solutions of being more robust to pose and lighting variations. In this paper, we propose a new experimental protocol based on the UHDB11 dataset for benchmarking 3D-2D FR algorithms. This new experimental protocol allows for the study of the performance of a 3D-2D FR solution under pose and/or lighting variations. Furthermore, we also benchmarked two state of the art 3D-2D FR algorithms. One is based on the Annotated Deformable Model (using manually labeled landmarks in this paper) using manually labeled landmarks whereas the other makes use of Oriented Gradient Maps along with an automatic pose estimation through random forest.
AB - Asymmetric 3D-2D face recognition (FR) aims to recognize individuals from 2D face images using textured 3D face models in the gallery (or vice versa). This new FR scenario has the potential to be readily deployable in field applications while still keeping the advantages of 3D FR solutions of being more robust to pose and lighting variations. In this paper, we propose a new experimental protocol based on the UHDB11 dataset for benchmarking 3D-2D FR algorithms. This new experimental protocol allows for the study of the performance of a 3D-2D FR solution under pose and/or lighting variations. Furthermore, we also benchmarked two state of the art 3D-2D FR algorithms. One is based on the Annotated Deformable Model (using manually labeled landmarks in this paper) using manually labeled landmarks whereas the other makes use of Oriented Gradient Maps along with an automatic pose estimation through random forest.
UR - https://www.scopus.com/pages/publications/84881505534
U2 - 10.1109/FG.2013.6553819
DO - 10.1109/FG.2013.6553819
M3 - 会议稿件
AN - SCOPUS:84881505534
SN - 9781467355452
T3 - 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
BT - 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
PB - IEEE Computer Society
T2 - 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013
Y2 - 22 April 2013 through 26 April 2013
ER -