A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
The eNTERFACE'05 Audio-Visual Emotion Database
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Emotion Recognition Based on Joint Visual and Audio Cues
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Toward multimodal fusion of affective cues
Proceedings of the 1st ACM international workshop on Human-centered multimedia
Modeling naturalistic affective states via facial and vocal expressions recognition
Proceedings of the 8th international conference on Multimodal interfaces
Audio-visual spontaneous emotion recognition
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Audio-visual based emotion recognition-a new approach
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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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.