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Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
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An Adaptive Face Tracker with Application in Yawning Detection.

Aasim Khurshid1,2, Jacob Scharcanski2

  • 1Sidia Instituto de Ciencia e tecnologia, Amazonas, Manaus 69055-035, Brazil.

Sensors (Basel, Switzerland)
|March 19, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive face tracking system that minimizes errors using a divergence estimate and resyncing with Weighted-Constrained Local Models (W-CLM). This robust face tracking improves driver monitoring applications like yawning detection.

Keywords:
error predictionface trackingfeature extraction for emotion analysisfeatures resyncingincremental PCAonline learningyawning detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Accurate face tracking is crucial for applications like driver monitoring.
  • Existing face tracking methods can suffer from errors, leading to missed detections.
  • Real-time error detection and correction are needed for robust tracking systems.

Purpose of the Study:

  • To develop an adaptive face tracking scheme that compensates for operational errors.
  • To minimize face tracking errors and prevent indefinite loss of the tracked face.
  • To improve the accuracy and reliability of face tracking in challenging environments.

Main Methods:

  • Implemented a tracking divergence estimate to detect and minimize face tracking errors.
  • Utilized a resyncing mechanism based on Constrained Local Models (CLM) for error reduction.
  • Proposed and integrated a Weighted-Constrained Local Model (W-CLM) to enhance CLM feature search.

Main Results:

  • The proposed adaptive face tracking scheme demonstrated superior performance compared to state-of-the-art methods.
  • The system effectively minimized tracking errors through divergence estimation and W-CLM resyncing.
  • Successful application of the face tracking method in driver monitoring tasks, including yawning detection.

Conclusions:

  • The adaptive face tracking scheme offers improved robustness and accuracy.
  • The W-CLM mechanism enhances the reliability of facial landmark re-estimation.
  • The proposed method is effective for driver monitoring and can be integrated into systems like yawning detection.