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Simulation of Print-Scan Transformations for Face Images based on Conditional Adversarial Networks
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Datum
2020
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Gesellschaft für Informatik e.V.
Zusammenfassung
In many countries, printing and scanning of face images is frequently performed as part
of the issuance process of electronic travel documents, e.g., ePassports. Image alterations induced
by such print-scan transformations may negatively effect the performance of various biometric subsystems,
in particular image manipulation detection. Consequently, according training data is needed
in order to achieve robustness towards said transformations. However, manual printing and scanning
is time-consuming and costly.
In this work, we propose a simulation of print-scan transformations for face images based on a Conditional
Generative Adversarial Network (cGAN). To this end, subsets of two public face databases
are manually printed and scanned using different printer-scanner combinations. A cGAN is then
trained to perform an image-to-image translation which simulates the corresponding print-scan transformations.
The goodness of simulation is evaluated with respect to image quality, biometric sample
quality and performance, as well as human assessment.