Advanced Image Quality Assessment for Hand- and Fingervein Biometrics
- URL: http://arxiv.org/abs/2302.09973v2
- Date: Tue, 21 Feb 2023 10:55:57 GMT
- Title: Advanced Image Quality Assessment for Hand- and Fingervein Biometrics
- Authors: Simon Kirchgasser, Christof Kauba, Georg Wimmer and Andreas Uhl
- Abstract summary: Natural Scene Statistics commonly used in non-reference image quality measures are proposed as biometric quality indicators for vasculature images.
The experiments were conducted on a total of 13 publicly available finger and hand vein datasets.
- Score: 5.218882272051637
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Natural Scene Statistics commonly used in non-reference image quality
measures and a deep learning based quality assessment approach are proposed as
biometric quality indicators for vasculature images. While NIQE and BRISQUE if
trained on common images with usual distortions do not work well for assessing
vasculature pattern samples' quality, their variants being trained on high and
low quality vasculature sample data behave as expected from a biometric quality
estimator in most cases (deviations from the overall trend occur for certain
datasets or feature extraction methods). The proposed deep learning based
quality metric is capable of assigning the correct quality class to the
vaculature pattern samples in most cases, independent of finger or hand vein
patterns being assessed. The experiments were conducted on a total of 13
publicly available finger and hand vein datasets and involve three distinct
template representations (two of them especially designed for vascular
biometrics). The proposed (trained) quality measures are compared to a several
classical quality metrics, with their achieved results underlining their
promising behaviour.
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