Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Boosting Local Feature Based Classifiers for Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Multimodal Face Recognition: Combination of Geometry with Physiological Information
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A 2D Range Hausdorff Approach for 3D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Constructing dense correspondences for the analysis of 3D facial morphology
Pattern Recognition Letters
Using Geodesic Distances for 2D-3D and 3D-3D Face Recognition
ICIAPW '07 Proceedings of the 14th International Conference of Image Analysis and Processing - Workshops
Face recognition using ada-boosted gabor features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
3D facial feature localization for registration
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Geometrical descriptors for human face morphological analysis and recognition
Robotics and Autonomous Systems
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In this paper, we present a novel method for 3D face recognition using adaboosted geodesic distance features. Firstly, a generic model is finely conformed to each face model contained within a 3D face dataset. Secondly, the geodesic distance between anatomical point pairs are computed across each conformed generic model. Adaboost then generates a strong-classifier based on a collection of geodesic distances that are most discriminative for face recognition. Experiments conducted on the Face Recognition Grand Challenge (FRGC) database D collection indicate that the system can achieve over a 95% rank-one recognition rate.