IEEE Transactions on Pattern Analysis and Machine Intelligence
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Extraction and Selection for Inferring User Engagement in an HCI Environment
Proceedings of the 13th International Conference on Human-Computer Interaction. Part I: New Trends
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Authentic facial expression analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Adaptive view-based appearance models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Facial action recognition for facial expression analysis from static face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning from examples in the small sample case: face expression recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A real-time automated system for the recognition of human facial expressions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
CVPRW '13 Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
Video analysis of approach-avoidance behaviors of teenagers speaking with virtual agents
Proceedings of the 15th ACM on International conference on multimodal interaction
Proceedings of the 2013 on Emotion recognition in the wild challenge and workshop
2013 International Conference on Multimodal Interaction
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Several vision-based systems for automatic recognition of emotion have been proposed in the literature. However most of these systems are evaluated only under controlled laboratory conditions. These controlled conditions poorly represent the constraints faced in real-world ecological situations. In this paper, two studies are described. In the first study we evaluate whether two robust vision-based measures (approach-avoidance detection and quantity of motion) can be used to discriminate between different emotions in a dataset containing acted facial expressions under uncontrolled conditions. In the second study we evaluate in the same dataset the accuracy of a commercially available software used for automatic emotion recognition under controlled conditions. Results showed that the evaluated measures are able to discriminate different emotions in uncontrolled conditions. In addition, the accuracy of the commercial software evaluated is reported.