Advanced Image Quality Assessment for Hand- and Fingervein Biometrics

In this publication several vein quality metrics have been evaluated as an extention of Remy22b. Furthermore, a deep learning based method for vein quality estimation was proposed, analysed and the results compared to other quality assessment methods in the field of vascular biometrics.

Abstract

Natural Scene Statistics commonly used in non-reference image quality measures and a proposed deep learning based quality assessment approach are suggested 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). A deep learning based quality metric is proposed in this work and designed to be capable of assigning the correct quality class to the vasculature pattern samples in most cases, independent of finger or hand vein patterns being assessed. The experiments, evaluating NIQE, BRISQUE and the newly proposed deep learning quality metric, 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 measure(s) are compared to several classical quality metrics, with their achieved results underlining their promising behaviour.

Reference

[Kirchgasser24a ]

Code and Results

Here it is possible to download the code for the proposed deep learning based quality metric and the evaluation results of the various vein quality metrics evaluated in the scope of the publication. The corresponding files are included in the downloadable .zip file as well.