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Extra Facial Landmark Localization via Global Shape Reconstruction.

Shuqiu Tan1, Dongyi Chen1, Chenggang Guo1

  • 1School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China.

Computational Intelligence and Neuroscience
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Summary

This study introduces a global shape reconstruction method to accurately locate extra facial landmarks. The approach reduces model size and training time while effectively handling pose variations and occlusions in face analysis.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Biometrics

Background:

  • Facial landmark localization is crucial for face analysis.
  • Current methods struggle with pose variations, occlusions, and model efficiency.
  • Need for robust and efficient facial landmark detection.

Purpose of the Study:

  • To present a global shape reconstruction method for locating extra facial landmarks.
  • To improve efficiency and reduce model size in facial landmark detection.
  • To address challenges of pose variations and occlusions.

Main Methods:

  • Decomposition of reduced facial landmark configurations into sparse coefficients.
  • Exploiting face shape correlations to regress between different landmark configurations.
  • Reconstruction of extra facial landmarks using a pretrained shape dictionary and sparse coefficient approximation.

Main Results:

  • Significant reduction in training time and model size for existing methods.
  • Minor compromise in detection accuracy compared to traditional methods.
  • Successful reconstruction of extra facial landmarks even with highly asymmetrical face poses.

Conclusions:

  • The proposed global shape reconstruction method is feasible and effective.
  • Enables more efficient and scalable facial landmark localization.
  • Offers a robust solution for handling challenging facial poses and occlusions.