emotion recognition using bimodal data fusion
This paper proposes a bimodal system for emotion recognition that uses face and speech analysis. Hidden Markov models - HMMs are used to learn and to describe the temporal dynamics of the emotion clues in the visual and acoustic channels. This approach provides a powerful method enabling to fuse the data we extract from separate modalities. The paper presents the best performing models and the results of the proposed recognition system.
emotion recognition bimodal data fusion