This work presents an investigation into recognizing emotions of people in near real life scenarios. Most existing studies on recognizing emotions of people have been conducted under controlled environments where the emotions are not spontaneous, rather highly exaggerated, and the number of modalities considered and their interactions is limited. The proposed bimodal approach fuses facial expression recognition (FER) with the “semantic orientation” of dialogs of actors to identify emotions under difficult illumination conditions, pose variations and occlusions in scenes. Experiments conducted on a dataset of 700 video clips from 17 movies demonstrate that the proposed fusion approach improves emotion recognition performance over unimodal approaches.
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