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In this paper, cross-corpora evaluations are used to analyze the bias of spontaneous facial expression databases. Local binary pattern and Gabor feature are extracted from difference-image sequences. A Hidden Markov Model is used as the classifier to discriminate arousal (i.e. high versus low) and valence (i.e. positive versus negative) respectively. Four datasets are adopted, including: UT-Dallas,...
Videos may induce users' mixture emotions. Most present emotional tagging research ignore the phenomena of multiple emotions' coexistence and mutual exclusion. In this paper, we propose a novel emotional tagging approach by exploring multiple emotion's relations. First, several visual and audio features are extracted from videos. Second, support vector machines are used as the classifiers to get the...
In this paper, cross-corpora evaluations are used to analyze the bias of spontaneous facial expression databases. Local binary pattern, Gabor, eigenface and fisherface features are extracted and applied to the four spontaneous expression databases: USTC-NVIE, VAM, Belfast Naturalistic and SEMAINE to recognize arousal (high/low) and valance (positive/negative) respectively. Experimental results indicate...
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