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 binary neural k-nearest neighbour technique
Knowledge and Information Systems
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Combining local features for robust nose location in 3D facial data
Pattern Recognition Letters
3D face detection using curvature analysis
Pattern Recognition
Automatic 3D Face Detection, Normalization and Recognition
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Automatic 3D facial segmentation and landmark detection
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
IEEE Transactions on Computers
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
Computer Vision and Image Understanding
Random Forests for Real Time 3D Face Analysis
International Journal of Computer Vision
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In this paper, a methodology for facial feature identification and localization approach is proposed based on binary neural network algorithms. We present a head pose and facial expression invariant 3D shape descriptor called Mesh-like Multi Circle Curvature Descriptor (MMCCD), which provides more 3D curvature attributes than other similar approaches. To search and match the feature patterns with more attributes, we use Advanced Uncertain Reasoning Architecture (AURA) k-Nearest Neighbour algorithms to encode, train and match the feature patterns based on 3D shape curvature. Experiments performed on the FRGC dataset (4950 3D faces) with pose and expression variations show that our approach IS able to achieve an accurate (over 99.69% nose tip identification) and robust identification and localization of facial features.