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
Computer Graphics and Virtual Environments: From Realism to Real - Time
Computer Graphics and Virtual Environments: From Realism to Real - Time
Face Recognition from 3D Data using Iterative Closest Point Algorithm and Gaussian Mixture Models
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
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 Tensors for Pose Invariant Automatic 3D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Preliminary Face Recognition Grand Challenge Results
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition
Computer Vision and Image Understanding
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining local features for robust nose location in 3D facial data
Pattern Recognition Letters
Three-Dimensional Face Recognition Using Shapes of Facial Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic 3D Face Detection, Normalization and Recognition
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Integration of local and global geometrical cues for 3D face recognition
Pattern Recognition
Deformation Modeling for Robust 3D Face Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Intrinsic Framework for Analysis of Facial Surfaces
International Journal of Computer Vision
Face recognition by matching 2D and 3D geodesic distances
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
A survey of 3d face recognition methods
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A Region Ensemble for 3-D Face Recognition
IEEE Transactions on Information Forensics and Security
Elastic radial curves to model 3D facial deformations
Proceedings of the ACM workshop on 3D object retrieval
2D representation of facial surfaces for multi-pose 3D face recognition
Pattern Recognition Letters
Efficient 3D face recognition handling facial expression and hair occlusion
Image and Vision Computing
Selecting 3D curves on the nasal surface using AdaBoost for person authentication
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
Computer Vision and Image Understanding
A structured template based 3D face recognition approach
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Multi-pose 3D face recognition based on 2D sparse representation
Journal of Visual Communication and Image Representation
An efficient 3D face recognition approach using local geometrical signatures
Pattern Recognition
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Among the many 3D face matching techniques that have been developed, are variants of 3D facial curve matching, which reduce the amount of face data to one or a few 3D curves. The face's central profile, for instance, proved to work well. However, the selection of the optimal set of 3D curves and the best way to match them has not been researched systematically. We propose a 3D face matching framework that allows profile and contour based face matching. Using this framework we evaluate profile and contour types including those described in the literature, and select subsets of facial curves for effective and efficient face matching. With a set of eight geodesic contours we achieve a mean average precision (MAP) of 0.70 and 92.5% recognition rate (RR) on the 3D face retrieval track of the Shape Retrieval Contest (SHREC'08), and a MAP of 0.96 and 97.6% RR on the University of Notre Dame (UND) test set. Face matching with these curves is time-efficient and performs better than other sets of facial curves and depth map comparison.