Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Quality
Biying Fu, Naser Damer

TL;DR
This paper investigates how face morphing attacks affect image quality and face recognition utility, and proposes an unsupervised detection method based on quality measures that achieves over 70% accuracy.
Contribution
It provides an extensive analysis of morphing effects on face image quality across multiple techniques and sources, and introduces a generalized unsupervised detection approach using quality scores.
Findings
Quality measures can distinguish morphing from bona fide images
Certain quality metrics enable unsupervised morphing attack detection
Detection accuracy exceeds 70% using selected quality measures
Abstract
Morphing attacks are a form of presentation attacks that gathered increasing attention in recent years. A morphed image can be successfully verified to multiple identities. This operation, therefore, poses serious security issues related to the ability of a travel or identity document to be verified to belong to multiple persons. Previous works touched on the issue of the quality of morphing attack images, however, with the main goal of quantitatively proofing the realistic appearance of the produced morphing attacks. We theorize that the morphing processes might have an effect on both, the perceptual image quality and the image utility in face recognition (FR) when compared to bona fide samples. Towards investigating this theory, this work provides an extensive analysis of the effect of morphing on face image quality, including both general image quality measures and face image utility…
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Taxonomy
TopicsFace recognition and analysis · Facial Nerve Paralysis Treatment and Research · Biometric Identification and Security
MethodsEmirates Airlines Office in Dubai · MagFace
