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Seeing through disguise: Getting to know you with a deep convolutional neural network.

Eilidh Noyes1, Connor J Parde2, Y Ivette Colón2

  • 1University of Huddersfield, Huddersfield, United Kingdom.

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We developed a deep convolutional neural network (DCNN) model to understand face recognition and disguise. Our model successfully mimics human abilities to identify faces, even when disguised, by learning identity clusters and contrasts.

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Area of Science:

  • Cognitive Science
  • Computer Vision
  • Artificial Intelligence

Background:

  • Disguise presents challenges for face recognition, impacting both identity consistency (evasion) and differentiation (impersonation).
  • Personal familiarity enhances human ability to recognize faces despite disguise.
  • Deep convolutional neural networks (DCNNs) offer a computational framework for modeling high-level visual learning in face identification.

Purpose of the Study:

  • To propose and test a computational model of facial familiarity using DCNNs.
  • To investigate how DCNNs process disguise and familiarity.
  • To explore mechanisms for grouping same-identity images and separating different identities.

Main Methods:

  • Trained a DCNN for face identification.
  • Assessed DCNN performance on disguised faces (unfamiliar condition).
  • Simulated familiarity by averaging identity representations and employed contrast learning techniques.

Main Results:

  • DCNN performance was degraded by disguise, mirroring human responses.
  • Simulated familiarity improved recognition for evasion disguise but hindered differentiation of similar identities.
  • Contrast learning enhanced DCNN performance for both evasion and impersonation disguises.

Conclusions:

  • DCNNs can model the effects of disguise on face recognition.
  • Simulated familiarity and contrast learning are crucial for robust face recognition models.
  • High-level visual representations in DCNNs support simultaneous clustering and separation of identities, crucial for familiar face recognition.