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Classifying Facial Actions
Gianluca Donato1, Marian Stewart Bartlett, Joseph C Hager
1Digital Persona, 805 Veterans Blvd., Suite 322, Redwood City, CA 94063.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|December 29, 2010
Summary
Automated facial action recognition systems show high accuracy. Gabor wavelets and independent component analysis achieved 96% accuracy in classifying 12 facial actions, outperforming human coders.
Area of Science:
- Computer Vision
- Human-Computer Interaction
- Affective Computing
Background:
- The Facial Action Coding System (FACS) quantifies facial movements but relies on time-consuming manual coding by experts.
- Accurate and automated facial action recognition is crucial for behavioral analysis, emotion research, and social interaction studies.
Purpose of the Study:
- To explore and compare various automated techniques for recognizing facial actions in image sequences.
- To evaluate the performance of these automated systems against human expert coders.
Main Methods:
- Optical flow analysis for facial motion.
- Holistic spatial analyses including PCA, ICA, LFA, and LDA.
- Methods utilizing local filter outputs like Gabor wavelets and local principal components.
Main Results:
- Gabor wavelet representation and independent component representation achieved 96% accuracy.
- These automated methods successfully classified 12 facial actions of the upper and lower face.
- Performance was compared against both naive and expert human subjects.
Conclusions:
- Automated facial action recognition is feasible and highly accurate.
- Local filters, high spatial frequencies, and statistical independence are key for effective facial action classification.
- Automated systems show potential to match or exceed human expert performance in FACS coding.
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