A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Depth vs. Intensity: Which is More Important for Face Recognition?
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Integrating Range and Texture Information for 3D Face Recognition
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Face Recognition
International Journal of Computer Vision
Matching 2.5D Face Scans to 3D Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Preliminary Face Recognition Grand Challenge Results
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
New Experiments on ICP-Based 3D Face Recognition and Authentication
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Enhancing 3D Face Recognition By Mimics Segmentation
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 03
Three-Dimensional Face Recognition Using Shapes of Facial Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D face recognition based on local shape patterns and sparse representation classifier
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
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The purpose of this paper is to study the influence of face expressions on the performance of a 3D face recognition algorithm. Three facial surface matching based algorithms, namely ICP, Localized ICP (L-ICP) and Region-based ICP (R-ICP), are benchmarked on several sets of data : the two first sets with neutral faces and the last with expressive ones. Results show that the R-ICP algorithm provides more robustness to face expression verification than the two other approaches.