Affective computing
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
ELIZA—a computer program for the study of natural language communication between man and machine
Communications of the ACM
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
2002 Index, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24
IEEE Transactions on Pattern Analysis and Machine Intelligence
Formational Parameters and Adaptive Prototype Instantiation for MPEG-4 Compliant Gesture Synthesis
CA '02 Proceedings of the Computer Animation
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The production and recognition of emotions in speech: features and algorithms
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Automatic recognition of facial expressions using hidden markov models and estimation of expression intensity
Joint processing of audio-visual information for the recognition of emotional expressions in human-computer interaction
SVM-based Nonparametric Discriminant Analysis, An Application to Face Detection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Toward computers that recognize and respond to user emotion
IBM Systems Journal
Handbook Of Pattern Recognition And Computer Vision
Handbook Of Pattern Recognition And Computer Vision
Affective multimodal human-computer interaction
Proceedings of the 13th annual ACM international conference on Multimedia
Human-Centered Multimedia: Culture, Deployment, and Access
IEEE MultiMedia
Neural Networks - Special issue: Emotion and brain
Parameterized facial expression synthesis based on MPEG-4
EURASIP Journal on Applied Signal Processing
Locating and extracting the eye in human face images
Pattern Recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Multi-stream confidence analysis for audio-visual affect recognition
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Emotion analysis in man-machine interaction systems
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Automatic temporal segment detection and affect recognition from face and body display
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Human computing and machine understanding of human behavior: a survey
ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Sentic avatar: multimodal affective conversational agent with common sense
Proceedings of the Third COST 2102 international training school conference on Toward autonomous, adaptive, and context-aware multimodal interfaces: theoretical and practical issues
Proceedings of the 14th ACM international conference on Multimodal interaction
Proceedings of the 14th ACM international conference on Multimodal interaction
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Affective and human-centered computing have attracted a lot of attention during the past years, mainly due to the abundance of devices and environments able to exploit multimodal input from the part of the users and adapt their functionality to their preferences or individual habits. In the quest to receive feedback from the users in an unobtrusive manner, the combination of facial and hand gestures with prosody information allows us to infer the users' emotional state, relying on the best performing modality in cases where one modality suffers from noise or bad sensing conditions. In this paper, we describe a multi-cue, dynamic approach to detect emotion in naturalistic video sequences. Contrary to strictly controlled recording conditions of audiovisual material, the proposed approach focuses on sequences taken from nearly real world situations. Recognition is performed via a 'Simple Recurrent Network' which lends itself well to modeling dynamic events in both user's facial expressions and speech. Moreover this approach differs from existing work in that it models user expressivity using a dimensional representation of activation and valence, instead of detecting discrete 'universal emotions', which are scarce in everyday human-machine interaction. The algorithm is deployed on an audiovisual database which was recorded simulating human-human discourse and, therefore, contains less extreme expressivity and subtle variations of a number of emotion labels.