Understanding Cross Domain Presentation Attack Detection for Visible
Face Recognition
- URL: http://arxiv.org/abs/2111.02548v1
- Date: Wed, 3 Nov 2021 22:25:45 GMT
- Title: Understanding Cross Domain Presentation Attack Detection for Visible
Face Recognition
- Authors: Jennifer Hamblin, Kshitij Nikhal, Benjamin S. Riggan
- Abstract summary: Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control.
presentation attacks on facial recognition systems have become increasingly sophisticated.
We propose a method that exploits information from infrared imagery during training to increase the discriminability of visible-based presentation attack detection systems.
- Score: 6.383297609254719
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Face signatures, including size, shape, texture, skin tone, eye color,
appearance, and scars/marks, are widely used as discriminative, biometric
information for access control. Despite recent advancements in facial
recognition systems, presentation attacks on facial recognition systems have
become increasingly sophisticated. The ability to detect presentation attacks
or spoofing attempts is a pressing concern for the integrity, security, and
trust of facial recognition systems. Multi-spectral imaging has been previously
introduced as a way to improve presentation attack detection by utilizing
sensors that are sensitive to different regions of the electromagnetic spectrum
(e.g., visible, near infrared, long-wave infrared). Although multi-spectral
presentation attack detection systems may be discriminative, the need for
additional sensors and computational resources substantially increases
complexity and costs. Instead, we propose a method that exploits information
from infrared imagery during training to increase the discriminability of
visible-based presentation attack detection systems. We introduce (1) a new
cross-domain presentation attack detection framework that increases the
separability of bonafide and presentation attacks using only visible spectrum
imagery, (2) an inverse domain regularization technique for added training
stability when optimizing our cross-domain presentation attack detection
framework, and (3) a dense domain adaptation subnetwork to transform
representations between visible and non-visible domains.
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