ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Keypoint Detection and Local Feature Matching for Textured 3D Face Recognition
International Journal of Computer Vision
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Robust 3D Face Recognition by Local Shape Difference Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Face Recognition Using Isogeodesic Stripes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Facial Symmetry to Handle Pose Variations in Real-World 3D Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
SHREC 2011: robust feature detection and description benchmark
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
SHREC'11 track: 3D face models retrieval
EG 3DOR'11 Proceedings of the 4th Eurographics conference on 3D Object Retrieval
Computer Vision and Image Understanding
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In this work, an original solution to 3D face identification is proposed, which supports recognition also in the case of probes with missing parts. Distinguishing traits of the face are captured by first extracting 3D keypoints of a face scan, then measuring how the face surface changes in the keypoints neighborhood using a local descriptor. To this end, an adaptation of the meshDOG algorithm to the case of 3D faces is proposed, together with a multi-ring geometric histogram descriptor. Face similarity is then evaluated by comparing local keypoint descriptors across inlier pairs of matching keypoints between probe and gallery scans. Experiments have been performed to assess the keypoints distribution and repeatability. Recognition accuracy of the proposed approach has been evaluated on the Bosphorus database, showing competitive results with respect to existing 3D face biometrics solutions.