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The Effect of Face Morphing on Face Image Quality

: Fu, Biying; Spiller, Noémie Catherine Hélène; Chen, Cong; Damer, Naser


Brömme, Arslan (Editor); Busch, Christoph (Editor); Damer, Naser; Dantcheva, Antitza; Gomez-Barrero, M.; Raja, K.; Rathgeb, Christian; Sequeira, A.F.; Uhl, Andreas ; Gesellschaft für Informatik -GI-, Bonn:
BIOSIG 2021, 20th International Conference of the Biometrics Special Interest Group. Proceedings : 15.-17. September 2021, International Digital Conference
Bonn: GI, 2021 (GI-Edition - Lecture Notes in Informatics (LNI). Proceedings P-315)
ISBN: 978-3-88579-709-8
DOI: 10.1109/BIOSIG52210.2021
5 S.
Gesellschaft für Informatik, Special Interest Group on Biometrics and Electronic Signatures (BIOSIG International Conference) <11, 2012, Darmstadt>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
Fraunhofer IGD ()
Lead Topic: Smart City; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); Research Line: Machine Learning (ML); biometrics; face recognition; deep learning; Morphing Attack; quality estimation; CRISP; ATHENE

Face morphing poses high security risk, which motivates the work on detection algorithms, as well as on anticipating novel morphing approaches. Using the statistical and perceptual image quality of morphed images in previous works has shown no clear correlation between the image quality and the realistic appearance. This motivated our study on the effect of face morphing on image quality and utility, we, therefore, applied eight general image quality metrics and four face-specific image utility metrics. We showed that MagFace (face utility metric) shows a clear difference between the bona fide and the morph images, regardless if they were digital or re-digitized. While most quality and utility metrics do not capture the artifacts introduced by the morphing process. Acknowledged that morphing artifacts are more apparent in certain areas of the face, we further investigated only these areas, for instance, tightly cropped face, nose, eyes, and mouth regions. We found that especially close to the eyes and the nose regions, using general image quality metrics as MEON and dipIQ can capture the image quality deterioration introduced by the morphing process.