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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Coupled Gaussian processes for pose-invariant facial expression recognition.

Ognjen Rudovic1, Maja Pantic, Ioannis Yiannis Patras

  • 1Department of Computing, Imperial College London, 180 Queen’s Gate, London SW7 2AZ, United Kingdom. o.rudovic@imperial.ac.uk

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 20, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method for facial expression recognition that remains accurate despite head movement. The Coupled Scaled Gaussian Process Regression (CSGPR) model normalizes head poses, improving recognition accuracy for expressive faces across various rotations.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Facial expression recognition is crucial for human-computer interaction.
  • Head pose variations significantly challenge the accuracy of facial expression recognition systems.
  • Existing methods struggle with unconstrained head poses and imbalanced datasets.

Purpose of the Study:

  • To develop a head-pose invariant facial expression recognition method.
  • To introduce a novel Coupled Scaled Gaussian Process Regression (CSGPR) model for robust head-pose normalization.
  • To enable accurate facial expression recognition across a wide range of head rotations (-45° to +45° pan, -30° to +30° tilt).

Main Methods:

  • Utilizing characteristic facial points for expression analysis.
  • Learning pose-specific mappings and coupling them to capture inter-pose dependencies.
  • Employing a gating function based on head-pose estimation for inference.
  • Training on a small set of discrete poses to generalize to continuous head movements.

Main Results:

  • The CSGPR model outperforms state-of-the-art methods including regression-based approaches, 2D/3D Point Distribution Models (PDMs), and Active Appearance Models (AAMs).
  • Superior performance is observed particularly with unknown poses and imbalanced training data.
  • The method demonstrates effectiveness for expressive faces across significant pan and tilt rotations.

Conclusions:

  • The proposed CSGPR method offers a robust solution for head-pose invariant facial expression recognition.
  • This approach successfully handles continuous head pose changes using discrete pose training data.
  • The method shows promise for real-world applications involving spontaneous and acted facial expressions under varying head orientations.