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• W2152955420 abstract "This paper proposes subject independent manifold features for dynamic emotion recognition. Facial action features, based on FACs, are firstly embedded into a low-dimensional manifold space using the ISOMAP algorithm, then the manifold features from different subjects are aligned into a global coordinate space by the supervised ISOMAP algorithm for recognition. To validate and evaluate the proposed manifold representation for emotion recognition, experiments with GMMs are presented. Given a new expression sequence, and tracked facial features, we are able to pin-point the actual occurrence of specific expressions, while characterizing its intensity by considering different expression temporal transition characteristics. Finally, experimental results show that our approach is able to separate different expressions successfully." @default.
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• W2152955420 date "2009-09-01" @default.
• W2152955420 modified "2023-09-23" @default.
• W2152955420 title "Manifold Analysis for Subject Independent Dynamic Emotion Recognition in Video Sequences" @default.
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• W2152955420 doi "https://doi.org/10.1109/icig.2009.76" @default.
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