Eye-State Action Unit Detection by Gabor Wavelets
ICMI '00 Proceedings of the Third International Conference on Advances in Multimodal Interfaces
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
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
Visual-Based Emotion Detection for Natural Man-Machine Interaction
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
HOG-Based Decision Tree for Facial Expression Classification
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Recognizing facial actions using Gabor wavelets with neutral face average difference
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Independent component analysis in a facial local residue space
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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Most automatic facial expression analysis systems try to analyze emotion categories. However, psychologists argue that there is no straightforward way to classify emotions from facial expressions. Instead, they propose FACS (Facial Action Coding System), a de-facto standard for categorizing facial actions independent from emotional categories.We describe a system that recognizes asymmetric FACS Action Unit activities and intensities without the use of markers. Facial expression extraction is achieved by difference images that are projected into a sub-space using either PCA or ICA, followed by nearest neighbor classification. Experiments show that this holistic approach achieves a recognition performance comparable to marker-based facial expression analysis systems or human FACS experts for a single-subject database recorded under controlled conditions.