Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective computing
Analysis of emotion recognition using facial expressions, speech and multimodal information
Proceedings of the 6th international conference on Multimodal interfaces
The eNTERFACE'05 Audio-Visual Emotion Database
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Analysis of vector space model and spatiotemporal segmentation for video indexing and retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Bi-modal emotion recognition from expressive face and body gestures
Journal of Network and Computer Applications
Using noninvasive wearable computers to recognize human emotions from physiological signals
EURASIP Journal on Applied Signal Processing
SAMMI: semantic affect-enhanced multimedia indexing
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Classifier fusion: combination methods for semantic indexing in video content
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
ICSR'10 Proceedings of the Second international conference on Social robotics
Affective speaker state analysis in the presence of reverberation
International Journal of Speech Technology
Human face analysis: from identity to emotion and intention recognition
ICEB'10 Proceedings of the Third international conference on Ethics and Policy of Biometrics and International Data Sharing
Proceedings of the 14th ACM international conference on Multimodal interaction
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Automatic recognition of human affective states is still a largely unexplored and challenging topic. Even more issues arise when dealing with variable quality of the inputs or aiming for real-time, unconstrained, and person independent scenarios. In this paper, we explore audio-visual multimodal emotion recognition. We present SAMMI, a framework designed to extract real-time emotion appraisals from non-prototypical, person independent, facial expressions and vocal prosody. Different probabilistic method for fusion are compared and evaluated with a novel fusion technique called NNET. Results shows that NNET can improve the recognition score (CR + ) of about 19% and the mean average precision of about 30% with respect to the best unimodal system.