Find the Differences: Differential Morphing Attack Detection vs Face Recognition
Authors: Una M. Kelly, Luuk J. Spreeuwers, Raymond N. J. Veldhuis
Organizations: Data Management and Biometrics, University of Twente, Enschede, the Netherlands · Machine Learning and Data Engineering, University of Münster, Münster, Germany · Department of Information Security and Communication Technology, Norwegian University of Science and Technology, Gjøvik, Norway
Morphing is a challenge to face recognition (FR) for which several morphing attack detection solutions have been proposed. We argue that face recognition and differential morphing attack detection (D-MAD) in principle perform very similar tasks, which we support by comparing an FR system with two existing D-MAD approaches. We also show that currently used decision thresholds inherently lead to FR systems being vulnerable to morphing attacks and that this explains the tradeoff between performance on normal images and vulnerability to morphing attacks. We propose using FR systems that are already in place for morphing detection and introduce a new evaluation threshold that guarantees an upper limit to the vulnerability to morphing attacks - even of unknown types.