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This paper presents a neural network (NN) based multimodal fusion classifier for automatic emotion recognition. The audio and visual channels provide complementary information, so we utilize features from three behavioral cues: frontal-view facial expression, profile-view facial expression and vocalization (audio). The problem of interest is to recognize basic emotions, and we use dimensional representation...
This paper presents a novel approach for automatic audio-visual emotion recognition. The audio and visual channels provide complementary information for human emotional states recognition, and we utilize coupled HMM (CHMM) as model-level fusion method in our work. To further improve recognition accuracy, we design an AdaBoost-CHMM ensemble classifier which takes CHMM as component classifiers in adaptive...
This paper presents a novel approach for automatic visual speech recognition using Convolutional VEF snake and canonical correlations. The utterance image sequences of isolated Chinese words are recorded with a head-mounted camera, and we use Convolutional VEF snake model to detect and track lip boundary rapidly and accurately. Geometric and motion features are both extracted from lip contour sequences...
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