3D Human Face Recognition Using Point Signature
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Facial Expression Decomposition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Facial Expression Recognition Based on Gabor Wavelet Transformation and Elastic Templates Matching
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
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
Adaptive Rigid Multi-region Selection for Handling Expression Variation in 3D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Static topographic modeling for facial expression recognition and analysis
Computer Vision and Image Understanding
An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Expression Deformation Approach to Non-rigid 3D Face Recognition
International Journal of Computer Vision
Interactive analysis and synthesis of facial expressions based on personal facial expression space
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Bilinear Models for 3-D Face and Facial Expression Recognition
IEEE Transactions on Information Forensics and Security
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This paper presents a technique for decomposing an unseen 3D face under any facial expression into an estimated 3D neutral face and expression deformations (the shape residue between the non-neutral and the estimated neutral 3D face). We show that this decomposition gives a robust facial expression classification and improves the accuracy of an off-the-shelf 3D face recognition system. The proposed decomposition system is a multistage data-driven process in which training expression residues and neutral faces reciprocally guide the decomposition of the 3D face. A plausible decomposition was achieved. The shapes and the normals of the expression residue are used for expression classification while the neutral face estimates are used for expression robust face recognition. Experiments were performed on a large number of non-neutral scans and significant expression classification rates were achieved. Moreover, 6% increase in face recognition rate was achieved for probes with severe facial expressions.