Automated Facial Expression Recognition Based on FACS Action Units
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
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
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual lip activity detection and speaker detection using mouth region intensities
IEEE Transactions on Circuits and Systems for Video Technology
Lipreading with local spatiotemporal descriptors
IEEE Transactions on Multimedia
Coupled Gaussian process regression for pose-invariant facial expression recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Deformable Model Fitting by Regularized Landmark Mean-Shift
International Journal of Computer Vision
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The mouth region of human face possesses highly discriminative information regarding the expressions on the face. Facial expression analysis to infer the emotional state of a user becomes very challenging when the user talks, as most of the mouth actions while uttering certain words match with mouth shapes expressing various emotions. We introduce a novel unsupervised method to temporally segment talking faces1 from the faces displaying only emotions, and use the knowledge of talking face segments to improve emotion recognition. The proposed method uses integrated gradient histogram of local binary patterns to represent mouth features suitably and identifies temporal segments of talking faces online by estimating the uncertainties of mouth movements over a period of time. The algorithm accurately identifies talking face segments on a real-world database where talking and emotion happens naturally. Also, the emotion recognition system, using talking face cues, showed considerable improvement in recognition accuracy.