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In this paper, we investigate the effect of transfer of emotion-rich features between source and target networks on classification accuracy and training time in a multimodal setting for vision based emotion recognition. First, we propose emosource-a 6-layer Deep Belief Network (DBN), trained on popular emotion corpora for emotion classification. Second, we propose two 6-layer DBNs — emotarget and...
Emotion analysis and recognition has become an interesting topic of research among the computer vision research community. In this paper, we first present the emoF-BVP database of multimodal (face, body gesture, voice and physiological signals) recordings of actors enacting various expressions of emotions. The database consists of audio and video sequences of actors displaying three different intensities...
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