Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Multi-Modal Approach to Facial Feature Detection
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
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
A Prescreener for 3D Face Recognition Using Radial Symmetry and the Hausdorff Fraction
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
Landmark Localisation in 3D Face Data
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
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
Landmark Localisation in 3D Face Data
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Point-pair descriptors for 3D facial landmark localisation
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Experimental comparison among 3D innovative face recognition frameworks
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
3D human face description: landmarks measures and geometrical features
Image and Vision Computing
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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Our pose-invariant point-pair descriptors, which encode 3D shape between a pair of 3D points are described and evaluated. Two variants of descriptor are introduced, the first is the point-pair spin image, which is related to the classical spin image of Johnson and Hebert, and the second is derived from an implicit radial basis function (RBF) model of the facial surface. We call this a cylindrically sampled RBF (CSR) shape histogram. These descriptors can effectively encode edges in graph based representations of 3D shapes. Thus, they are useful in a wide range of 3D graph-based retrieval applications. Here we show how the descriptors are able to identify the nose-tip and the eye-corner of a human face simultaneously in six promising landmark localisation systems. We evaluate our approaches by computing root mean square errors of estimated landmark locations against our ground truth landmark localisations within the 3D Face Recognition Grand Challenge database.