Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Semi-supervised learning for facial expression recognition
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Semi-supervised graph clustering: a kernel approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Non-negative matrix factorization for semi-supervised data clustering
Knowledge and Information Systems
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Toward Practical Smile Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition on multiple manifolds
Pattern Recognition
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Appearance manifold of facial expression
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
An associate-predict model for face recognition
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Smile Detection by Boosting Pixel Differences
IEEE Transactions on Image Processing
Improved facial expression recognition via uni-hyperplane classification
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Learning active facial patches for expression analysis
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Image and Vision Computing
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Facial expression recognition is an important task in human-computer interaction. Some methods work well on "lab-controlled" data. However, their performances degenerate dramatically on real-world data as expression covers large variations, including pose, illumination, occlusion, and even culture change. To deal with this problem, large scale data is definitely needed. On the other hand, collecting and labeling wild expression data can be difficult and time consuming. In this paper, aiming at robust expression recognition in wild which suffers from the mentioned problems, we propose a semi-supervised method to make use of the large scale unlabeled data in two steps: 1) We enrich reference manifolds using selected unlabeled data which are closed to certain kind of expression. The learned manifolds can help smooth the variation of original data and provide reliable metric to maintain semantic similarity of expression; 2) To elevate the original labeled set for enhanced training, we iteratively employ the semi-supervised clustering to assign labels for unlabeled data and add the most discriminant ones into the labeled set. Experiments on the latest wild expression database SFEW and GENKI show that the proposed method can effectively exploit unlabeled data to improve the performance on real-world expression recognition.