Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Spotting Segments Displaying Facial Expression from Image Sequences Using HMM
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Robust Pose Invariant Facial Feature Detection and Tracking in Real-Time
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Manifold based analysis of facial expression
Image and Vision Computing
A new framework for grayscale and colour non-lambertian shape-from-shading
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
Automatic facial expression recognition using facial animation parameters and multistream HMMs
IEEE Transactions on Information Forensics and Security
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This paper presents a 3D motion based approach to facial expression recognition from video sequences. A non-Lambertian shapefrom-shading (SFS) framework is used to recover 3D facial surfaces. The SFS technique avoids heavy computational requirements normally encountered by using a 3D face model. Then, a parametric motion model and optical flow are employed to obtain the nonrigid motion parameters of surface patches. At first, we obtain uniform motion parameters under the assumptions that motion due to change in expressions is temporally consistent. Then we relax the uniform motion constraint, and obtain temporal motion parameters. The two types of motion parameters are used to train and classify using Adaboost and HMM-based classifier. Experimental results show that temporal motion parameters perform much better than uniform motion parameters, and can be used to efficiently recognize facial expression.