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Head poses and grimaces: Challenges for automated face identification algorithms?

Petra Urbanova1, Tomas Goldmann2, Dominik Cerny1

  • 1Department of Anthropology, Faculty of Science, Masaryk University, Czech Republic.

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Automated facial identification accuracy decreases with head pose variations and certain expressions. Performance is notably impacted by upward head movements and differs between genders, with males showing higher dissimilarity scores.

Keywords:
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Area of Science:

  • Biometrics and Forensic Science
  • Computer Vision and Artificial Intelligence

Background:

  • Current biometric and commercial image processing heavily relies on AI and machine learning for accuracy.
  • The "black-box" nature of these AI systems raises concerns regarding transparency and accountability in forensic applications.

Purpose of the Study:

  • To investigate the impact of facial expressions and head poses on automated facial identification accuracy.
  • To analyze the performance of a state-of-the-art algorithm (ArcFace) under varying facial and pose conditions.

Main Methods:

  • Utilized 3D facial data from 41 participants, generating 2D images with nine expressions and head poses varying from -45° to 45°.
  • Conducted pair-wise comparisons using the ArcFace algorithm on over 54 million dissimilarity scores.
  • Analyzed the influence of yaw and pitch head movements, different expressions, and gender on identification accuracy.

Main Results:

  • Minor head deviations had minimal impact, but accuracy decreased significantly as targets deviated from the frontal view.
  • Upward pitch movements and right-to-left yaw movements were more detrimental than downward pitch and left-to-right yaw.
  • Males exhibited consistently higher dissimilarity scores than females, particularly with upward head movements.

Conclusions:

  • Facial expression and head pose significantly challenge automated facial identification systems, especially at extreme angles.
  • Gender differences in performance highlight potential biases in current algorithms.
  • Further research is needed to enhance the robustness and fairness of facial identification technologies in forensic and biometric contexts.