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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Huang, L., Xin, L., Zhao, L., & Tao, J. (2007). Combining audio and video by dominance in bimodal emotion recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4738 LNCS, pp. 729–730). Springer Verlag. https://doi.org/10.1007/978-3-540-74889-2_71
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