Recognizing Action Units for Facial Expression Analysis
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
Dual-State Parametric Eye Tracking
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Maxdiff kd-trees for data condensation
Pattern Recognition Letters
Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Mean field approach for tracking similar objects
Computer Vision and Image Understanding
Permutation Tests for Studying Classifier Performance
The Journal of Machine Learning Research
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A novel graph theoretic approach is proposed for recognizing each of the six basic prototypic human emotional facial expressions from a video sequence. The feature used, called a Shape-Motion-Descriptor (SMD), is based on orientation-quantized gradient-weighted optical flow in a hierarchical manner. The basis of the SMD is learned using a codebook learning technique. Inter-relations between SMDs are represented through a graph. A novel definition of de-centrality measure of graph connectivity is devised to make the initially large codebook, a compact one. Each video sequence is represented by an expression descriptor. The efficiency of the proposed expression descriptor is tested using different classifiers and the results are compared with the state-of-the-art methods in literature. Our result is at par or better than competing methods.