Active shape models—their training and application
Computer Vision and Image Understanding
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Motion segmentation and pose recognition with motion history gradients
Machine Vision and Applications - Special issue: IEEE WACV
Spotting Segments Displaying Facial Expression from Image Sequences Using HMM
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
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
Multimodal affect recognition in learning environments
Proceedings of the 13th annual ACM international conference on Multimedia
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Affect from Non-stylised Body Motions
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
Similarity Features for Facial Event Analysis
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic temporal segment detection and affect recognition from face and body display
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Hidden Part Models for Human Action Recognition: Probabilistic versus Max Margin
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a Minimal Representation of Affective Gestures
IEEE Transactions on Affective Computing
Learning from examples in the small sample case: face expression recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Editorial: Introduction To The Special Issue On Affect Analysis In Continuous Input
Image and Vision Computing
Learning a sparse codebook of facial and body microexpressions for emotion recognition
Proceedings of the 15th ACM on International conference on multimodal interaction
Image and Vision Computing
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Recently, recognizing affects from both face and body gestures attracts more attentions. However, it still lacks of efficient and effective features to describe the dynamics of face and gestures for real-time automatic affect recognition. In this paper, we combine both local motion and appearance feature in a novel framework to model the temporal dynamics of face and body gesture. The proposed framework employs MHI-HOG and Image-HOG features through temporal normalization or bag of words to capture motion and appearance information. The MHI-HOG stands for Histogram of Oriented Gradients (HOG) on the Motion History Image (MHI). It captures motion direction and speed of a region of interest as an expression evolves over the time. The Image-HOG captures the appearance information of the corresponding region of interest. The temporal normalization method explicitly solves the time resolution issue in the video-based affect recognition. To implicitly model local temporal dynamics of an expression, we further propose a bag of words (BOW) based representation for both MHI-HOG and Image-HOG features. Experimental results demonstrate promising performance as compared with the state-of-the-art. Significant improvement of recognition accuracy is achieved as compared with the frame-based approach that does not consider the underlying temporal dynamics.