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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
PubMed
Summary
This summary is machine-generated.

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.

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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.