IEEE Transactions on Pattern Analysis and Machine Intelligence
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
The Journal of Machine Learning Research
Supervised term weighting for automated text categorization
Proceedings of the 2003 ACM symposium on Applied computing
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topics over time: a non-Markov continuous-time model of topical trends
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
MedLDA: maximum margin supervised topic models for regression and classification
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Dynamic mixture models for multiple time series
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Term weighting schemes for Latent Dirichlet Allocation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Probabilistic expression analysis on manifolds
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A temporal latent topic model for facial expression recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
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This paper presents a discriminative temporal topic model (DTTM) for facial expression recognition. Our DTTM is developed by introducing temporal and categorical information into Latent Dirichlet Allocation (LDA) topic model. Temporal information is integrated by placing an asymmetric Dirichlet prior over document-topic distributions. The discriminative ability is improved by a supervised term weighting scheme. We describe the resulting DTTM in detail and show how it can be applied to facial expression recognition. Experiments on CMU expression database illustrate that the proposed DTTM is very effective in facial expression recognition.