Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Real time facial expression recognition in video using support vector machines
Proceedings of the 5th international conference on Multimodal interfaces
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently scaling up video annotation with crowdsourced marketplaces
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Multimedia Tools and Applications
Human computation: a survey and taxonomy of a growing field
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning invariance through imitation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
FaceTube: predicting personality from facial expressions of emotion in online conversational video
Proceedings of the 14th ACM international conference on Multimodal interaction
Multimodal analysis of the implicit affective channel in computer-mediated textual communication
Proceedings of the 14th ACM international conference on Multimodal interaction
Facing reality: an industrial view on large scale use of facial expression analysis
Proceedings of the 2013 on Emotion recognition in the wild challenge and workshop
Crowdsourcing facial expressions using popular gameplay
SIGGRAPH Asia 2013 Technical Briefs
BeFaced: a game for crowdsourcing facial expressions
SIGGRAPH Asia 2013 Symposium on Mobile Graphics and Interactive Applications
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In the past, collecting data to train facial expression and affect recognition systems has been time consuming and often led to data that do not include spontaneous expressions. We present the first crowdsourced data collection of dynamic, natural and spontaneous facial responses as viewers watch media online. This system allowed a corpus of 3,268 videos to be collected in under two months. We characterize the data in terms of viewer demographics, position, scale, pose and movement of the viewer within the frame, and illumination of the facial region. We compare statistics from this corpus to those from the CK+ and MMI databases and show that distributions of position, scale, pose, movement and luminance of the facial region are significantly different from those represented in these datasets. We demonstrate that it is possible to efficiently collect massive amounts of ecologically valid responses, to known stimuli, from a diverse population using such a system. In addition facial feature points within the videos can be tracked for over 90% of the frames. These responses were collected without need for scheduling, payment or recruitment. Finally, we describe a subset of data (over 290 videos) that will be available for the research community.