IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
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
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Dynamics of facial expression extracted automatically from video
Image and Vision Computing
Automatic facial expression recognition using facial animation parameters and multistream HMMs
IEEE Transactions on Information Forensics and Security
Expression recognition in videos using a weighted component-based feature descriptor
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Facial expression recognition from near-infrared videos
Image and Vision Computing
Towards a dynamic expression recognition system under facial occlusion
Pattern Recognition Letters
Exploring bag of words architectures in the facial expression domain
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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A novel framework of facial appearance and shape information extraction for facial expression recognition is proposed. For appearance extraction, a facial-component-based bag of words method is presented. We segment face images into 4 component regions, and sub-divide them into 4×4 sub-regions. Dense SIFT (Scale-Invariant Feature Transform) features are calculated over the sub-regions and vector quantized into 4×4 sets of codeword distributions. For shape extraction, PHOG (Pyramid Histogram of Orientated Gradient) descriptors are computed on the 4 facial component regions to obtain the spatial distribution of edges. Our framework provides holistic characteristics for the local texture and shape features by enhancing the structure-based spatial information, and makes the local descriptors be possible to be used in facial expression recognition for the first time. The recognition rate achieved by the fusion of appearance and shape features at decision level using the Cohn-Kanade database is 96.33%, which outperforms the state of the arts.