Affective computing
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Affective Computing
The first audio/visual emotion challenge and workshop: an introduction
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Automatic Recognition of Non-Acted Affective Postures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Robust continuous prediction of human emotions using multiscale dynamic cues
Proceedings of the 14th ACM international conference on Multimodal interaction
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Image and Vision Computing
Audiovisual three-level fusion for continuous estimation of Russell's emotion circumplex
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
Shape-based modeling of the fundamental frequency contour for emotion detection in speech
Computer Speech and Language
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In naturalistic behaviour, the affective states of a person change at a rate much slower than the typical rate at which video or audio is recorded (e.g. 25fps for video). Hence, there is a high probability that consecutive recorded instants of expressions represent a same affective content. In this paper, a multi-stage automatic affective expression recognition system is proposed which uses Hidden Markov Models (HMMs) to take into account this temporal relationship and finalize the classification process. The hidden states of the HMMs are associated with the levels of affective dimensions to convert the classification problem into a best path finding problem in HMM. The system was tested on the audio data of the Audio/Visual Emotion Challenge (AVEC) datasets showing performance significantly above that of a one-stage classification system that does not take into account the temporal relationship, as well as above the baseline set provided by this Challenge. Due to the generality of the approach, this system could be applied to other types of affective modalities.