Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Machine Learning
Model-Based Face Tracking for View-Independent Facial Expression Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic 3D Face Reconstruction based on Single 2D Image
MUE '07 Proceedings of the 2007 International Conference on Multimedia and Ubiquitous Engineering
Synthesis and recognition of facial expressions in virtual 3D views
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Facial expression recognition using fisher weight maps
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Facial action recognition for facial expression analysis from static face images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Automatic facial feature extraction by genetic algorithms
IEEE Transactions on Image Processing
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In this paper, we present a facial expression recognition method using feature-adaptive motion energy analysis. Our method is simplicity-oriented and avoids complicated face model representations or computationally expensive algorithms to estimate facial motions. Instead, the proposed method uses a simplified action-based face model to reduce the computational complexity of the entire facial expression analysis and recognition process. Feature-adaptive motion energy analysis estimates facial motions in a cost-effective manner by assigning more computational complexity on selected discriminative facial features. Facial motion intensity and orientation evaluation are then performed accordingly. Both facial motion intensity and orientation evaluation are based on simple calculations by exploiting a few motion energy values in the difference image, or optimizing the characteristics of feature-adaptive facial feature regions. For facial expression classification, a computationally inexpensive decision tree is used since the information gain heuristics of ID3 decision tree forces the classification to be done with minimal Boolean comparisons. The feasibility of the proposed method is shown through the experimental results as the proposed method recognized every facial expression in the JAFFE database by up to 75% with very low computational complexity.