Abstract
We propose a novel bimodal emotion recognition approach by using the boosting-based framework, in which we can automatically determine the adaptive weights for audio and visual features. In this way, we balance the dominances of audio and visual features dynamically in feature-level to obtain better performance.
The work is supported by the National Natural Science Foundation of China (No. 60575032) and the 863 Program (No. 2006AA01Z138).
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© 2007 Springer-Verlag Berlin Heidelberg
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Huang, L., Xin, L., Zhao, L., Tao, J. (2007). Combining Audio and Video by Dominance in Bimodal Emotion Recognition. In: Paiva, A.C.R., Prada, R., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2007. Lecture Notes in Computer Science, vol 4738. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74889-2_71
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DOI: https://doi.org/10.1007/978-3-540-74889-2_71
Publisher Name: Springer, Berlin, Heidelberg
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