Support Vector Machines for 3D Object Recognition
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry-based muscle modeling for facial animation
GRIN'01 No description on Graphics interface 2001
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
IEEE Transactions on Image Processing
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Automatic facial expression recognition based on spatiotemporal descriptors
Pattern Recognition Letters
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In this paper, we propose a new method for facial expression recognition. We utilize the Candide facial grid and apply Principal Components Analysis (PCA) to find the two eigenvectors of the model vertices. These eigenvectors along with the barycenter of the vertices are used to define a new coordinate system where vertices are mapped. Support Vector Machines (SVMs) are then used for the facial expression classification task. The method is invariant to in-plane translation and rotation as well as scaling of the face and achieves very satisfactory results.