Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
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
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
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
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
Facial action recognition for facial expression analysis from static face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Portable real time emotion detection system for the disabled
Expert Systems with Applications: An International Journal
Facial affect recognition using regularized discriminant analysis-based algorithms
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
Spatiotemporal-boosted DCT features for head and face gesture analysis
HBU'10 Proceedings of the First international conference on Human behavior understanding
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Automatic facial expression recognition using statistical-like moments
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Video indexing and recommendation based on affective analysis of viewers
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Automatic facial expression recognition based on spatiotemporal descriptors
Pattern Recognition Letters
Spatiotemporal features for effective facial expression recognition
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework
Image and Vision Computing
A review of motion analysis methods for human Nonverbal Communication Computing
Image and Vision Computing
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It is well known that how to extract dynamic features is a key issue for video-based face analysis. In this paper, we present a novel approach of facial expression recognition based on the encoded dynamic features. In order to capture the dynamic characteristics of facial events, we design the dynamic haar-like features to represent the temporal variations of facial appearance. Inspired by the binary pattern coding, we further encode the dynamic features into the binary pattern features, which are useful to construct weak classifiers for boosting learning. Finally, the Adaboost is performed to learn a set of discriminating encoded dynamic features for facial expression recognition. We conduct the experiments on the CMU expression database, and the experiment result shows the power of the proposed method. We also extend this method to the active units (AU) recognition, and get a promising performance.