On the Impact of Face Image Quality on Morphing Attack Detection

The morphing attack is widely acknowledged as an important security threat to face recognition systems in the context of electronic machine readable travel documents and several possible countermeasures have been recently proposed. Among the existing solutions, differential Morphing Attack Detection (MAD) algorithms, based on the comparison of the document image (possibly morphed) and a trusted live capture, proved to be quite effective and robust in detecting this kind of attack. However, deploying such solutions in a real-world operational scenario requires the capability of dealing with images of variable quality in terms of illumination, pose, focus, etc. This paper analyzes the impact of face image quality on MAD performance through an extensive image quality assessment, carried out on a large and realistic operational dataset using different state-of-the-art algorithms, thus providing useful insights for the development of more robust MAD systems.