Advanced Image Quality Assessment for Hand- and Fingervein Biometrics

TL;DR

This paper introduces a deep learning-based image quality assessment method tailored for vasculature images used in biometric identification, outperforming traditional metrics across multiple datasets.

Contribution

It proposes a novel deep learning approach for vascular image quality assessment, addressing limitations of existing non-reference metrics in biometric contexts.

Findings

Deep learning quality metric accurately classifies vasculature image quality.

Proposed methods outperform classical metrics on 13 datasets.

Effective across different finger and hand vein pattern representations.

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.