A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
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Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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ICML '04 Proceedings of the twenty-first international conference on Machine learning
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Numerical Recipes 3rd Edition: The Art of Scientific Computing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic facial expression recognition on a single 3D face by exploring shape deformation
MM '09 Proceedings of the 17th ACM international conference on Multimedia
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Signal Processing
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Data Mining and Knowledge Discovery
Facial expression recognition using 3D facial feature distances
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Static and dynamic 3D facial expression recognition: A comprehensive survey
Image and Vision Computing
A neural-AdaBoost based facial expression recognition system
Expert Systems with Applications: An International Journal
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3D facial expression recognition has great potential in human computer interaction and intelligent robot systems. In this paper, we propose a two-step approach which combines both the feature selection and the feature fusion techniques to choose more comprehensive and discriminative features for 3D facial expression recognition. In the feature selection stage, we utilize a novel normalized cut-based filter (NCBF) algorithm to select the high relevant and low redundant geometrically localized features (GLF) and surface curvature features (SCF), respectively. Then in the feature fusion stage, PCA is performed on the selected GLF and SCF in order to avoid the curse-of-dimensionality challenge. Finally, the processed GLF and SCF are fused together to capture the most discriminative information in 3D expressional faces. Experiments are carried out on the BU-3DFE database, and the proposed approach outperforms the conventional methods by providing more competitive results.