Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Evaluation of Face Resolution for Expression Analysis
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Haar Features for FACS AU Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of affect recognition methods: audio, visual and spontaneous expressions
Proceedings of the 9th international conference on Multimodal interfaces
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity Features for Facial Event Analysis
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
Learning personal specific facial dynamics for face recognition from videos
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Facial event classification with task oriented dynamic Bayesian network
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
From facial expression to level of interest: a spatio-temporal approach
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Investigating the dynamics of facial expression
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Facial expression analysis using nonlinear decomposable generative models
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Facial action recognition for facial expression analysis from static face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Facial movement based recognition
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
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In this paper, we propose a new feature: dynamic soft encoded pattern (DSEP) for facial event analysis. We first develop similarity features to describe complicated variations of facial appearance, which take similarities between a haar-like feature in a given image and the corresponding ones in reference images as feature vector. The reference images are selected from the apex images of facial expressions, and the k-means clustering is applied to the references. We further perform a temporal clustering on the similarity features to produce several temporal patterns along the temporal domain, and then we map the similarity features into DSEP to describe the dynamics of facial expressions, as well as to handle the issue of time resolution. Finally, boosting-based classifier is designed based on DSEPs. Different from previous works, the proposed method makes no assumption on the time resolution. The effectiveness is demonstrated by extensive experiments on the Cohn-Kanade database.