Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Three-Dimensional Face Recognition
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
Face Processing: Advanced Modeling and Methods
Face Processing: Advanced Modeling and Methods
Detection of Anchor Points for 3D Face Veri.cation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Unconstrained Face Recognition (International Series on Biometrics)
Unconstrained Face Recognition (International Series on Biometrics)
Automatic Feature Extraction for Multiview 3D Face Recognition
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
Combining local features for robust nose location in 3D facial data
Pattern Recognition Letters
Automatic 3D facial segmentation and landmark detection
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Point-pair descriptors for 3D facial landmark localisation
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Automatic 3d face feature points extraction with spin images
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
3D facial feature localization for registration
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Point-pair descriptors for 3D facial landmark localisation
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Regional registration for expression resistant 3-D face recognition
IEEE Transactions on Information Forensics and Security
Proceedings of the ACM workshop on 3D object retrieval
3D human face description: landmarks measures and geometrical features
Image and Vision Computing
3D facial landmark localization using combinatorial search and shape regression
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes
International Journal of Computer Vision
3D human face soft tissues landmarking method: An advanced approach
Computers in Industry
Geometry-based 3D face morphology analysis: soft-tissue landmark formalization
Multimedia Tools and Applications
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A comparison of several approaches that use graph matching and cascade filtering for landmark localization in 3D face data is presented. For the first method, we apply the structural graph matching algorithm “relaxation by elimination” using a simple “distance to local plane” node property and a “Euclidean distance” arc property. After the graph matching process has eliminated unlikely candidates, the most likely triplet is selected, by exhaustive search, as the minimum Mahalanobis distance over a six dimensional space, corresponding to three node variables and three arc variables. A second method uses state-of-the-art pose-invariant feature descriptors embedded into a cascade filter to localize the nose tip. After that, local graph matching is applied to localize the inner eye corners. We evaluate our systems by computing root mean square errors ofestimated landmark locations against ground truth landmark localizations within the 3D Face Recognition Grand Challenge database. Our best system, which uses a novel pose-invariant shape descriptor, scores 99.77% successful localization of the nose and 96.82% successful localization of the eyes.