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Can Facial Pose and Expression Be Separated with Weak Perspective Camera?

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The weak perspective camera model commonly used in facial analysis creates significant pose-expression ambiguity, leading to inaccurate facial expression recognition and Action Unit detection, even at greater distances.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Facial pose and expression analysis relies on 3D-to-2D camera models.
  • The weak perspective (WP) camera model is widely adopted in facial analysis due to assumed negligible errors at distance.
  • The validity of WP camera assumptions for facial analysis has not been empirically tested over nearly two decades.

Purpose of the Study:

  • To critically evaluate the suitability of the weak perspective camera model for separating facial pose and expression.
  • To theoretically and experimentally investigate the pose-expression ambiguity caused by the WP camera model.
  • To assess the impact of WP-induced errors on facial analysis applications like Action Unit (AU) detection.

Main Methods:

  • Theoretical analysis to demonstrate pose-expression ambiguity introduced by the WP camera model.
  • Experimental quantification of the magnitude of spurious expressions resulting from WP.
  • Testing the effect of spurious expressions on Action Unit (AU) detection performance.

Main Results:

  • The weak perspective camera model introduces significant pose-expression ambiguity, leading to spurious expression estimations.
  • Substantial false positive rates in Action Unit detection were observed, even when subjects are not in close proximity to the camera.
  • The characteristics of spurious expressions are influenced by the specific point distribution model used for expression representation.

Conclusions:

  • The conventional assumption of negligible errors with weak perspective cameras in facial analysis needs re-evaluation.
  • Facial analysis software should support and encourage the use of more accurate, true camera models.
  • Addressing WP-induced ambiguity is crucial for improving the reliability of facial expression and pose analysis.