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• W2608956662 abstract "In this paper is presented a novel multimodal emotion recognition system which is based on the analysis of audio and visual cues. MFCC-based features are extracted from the audio channel and facial landmark geometric relations are computed from visual data. Both sets of features are learnt separately using state-of-the-art classifiers. In addition, we summarise each emotion video into a reduced set of key-frames, which are learnt in order to visually discriminate emotions by means of a Convolutional Neural Network. Finally, confidence outputs of all classifiers from all modalities are used to define a new feature space to be learnt for final emotion prediction, in a late fusion/stacking fashion. The conducted experiments on eNTERFACE'05 database show significant performance improvements of our proposed system in comparison to state-of-the-art approaches." @default.
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• W2608956662 date "2016-12-01" @default.
• W2608956662 modified "2023-10-02" @default.
• W2608956662 title "Fusion of classifier predictions for audio-visual emotion recognition" @default.
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• W2608956662 doi "https://doi.org/10.1109/icpr.2016.7899608" @default.
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