An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A tone mapping algorithm for high contrast images
EGRW '02 Proceedings of the 13th Eurographics workshop on Rendering
Audio-visual based emotion recognition-a new approach
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
A robust multimodal approach for emotion recognition
Neurocomputing
Image ratio features for facial expression recognition application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
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In this paper, a novel system is proposed to recognize facial expression based on face sketch, which is produced by programmable graphics hardware-GPU(Graphics Processing Unit). Firstly, an expression subspace is set up from a corpus of images consisting of seven basic expressions. Secondly, by applying a GPU based edge detection algorithm, the real-time facial expression sketch extraction is performed. Subsequently, noise elimination is carried out by tone mapping operation on GPU. Then, an ASM instance is trained to track the facial feature points in the sketched face image more efficiently and precisely than that on a grey level image directly. Finally, by the normalized key feature points, Eigen expression vector is deduced to be the input of MSVM(Multi-SVMs) based expression recognition model, which is introduced to perform the expression classification. Test expression images are categorized by MSVM into one of the seven basic expression subspaces. Experiment on a data set containing 500 pictures clearly shows the efficacy of the algorithm.