Affective computing
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Human-Computer Studies - Application of affective computing in human—Computer interaction
Computer Animation and Virtual Worlds - Special Issue: The Very Best Papers from CASA 2004
Cross-cultural differences in recognizing affect from body posture
Interacting with Computers
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Fast learning in networks of locally-tuned processing units
Neural Computation
Convex multi-task feature learning
Machine Learning
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Exploring Fusion Methods for Multimodal Emotion Recognition with Missing Data
IEEE Transactions on Affective Computing
Overview and recent advances in partial least squares
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Automatic Recognition of Non-Acted Affective Postures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Understanding communicative emotions from collective external observations
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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An important challenge in building automatic affective state recognition systems is establishing the ground truth. When the groundtruth is not available, observers are often used to label training and testing sets. Unfortunately, inter-rater reliability between observers tends to vary from fair to moderate when dealing with naturalistic expressions. Nevertheless, the most common approach used is to label each expression with the most frequent label assigned by the observers to that expression. In this paper, we propose a general pattern recognition framework that takes into account the variability between observers for automatic affect recognition. This leads to what we term a multi-score learning problem in which a single expression is associated with multiple values representing the scores of each available emotion label. We also propose several performance measurements and pattern recognition methods for this framework, and report the experimental results obtained when testing and comparing these methods on two affective posture datasets.