Oliver Remy, Jutta Hämmerle-Uhl and Andreas UhlBrömme, ArslanDamer, NaserGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira Ana F.Todisco, MassimilianoUhl, Andreas2022-10-272022-10-272022978-3-88579-723-4https://dl.gi.de/handle/20.500.12116/39719Natural Scene Statistics as used in non-reference image quality measures are proposed to be used as fingervein sample quality indicators. While NIQE and BRISQUE trained on common images with usual distortions do not work well in the fingervein quality context, their variants being trained on high and low quality fingervein sample data behave as expected from a biometric quality estimator. Experiments involve two publicly available fingervein datasets and two distinct template representations. The proposed (trained) quality measures are compared to a set of classical fingervein quality metrics which underlines their highly promising behaviour.enVascular biometricsfingervein recognitionsample qualitynatural scene statisticsText/Conference Paper10.1109/BIOSIG55365.2022.98969741617-5476