Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.
%0 Journal Article
%1 s20051308
%A Jenadeleh, Mohsen
%A Pedersen, Marius
%A Saupe, Dietmar
%D 2020
%J Sensors
%K sfbtrr161 a05 from:christinawarren 2020
%N 5
%R 10.3390/s20051308
%T Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition
%U https://www.mdpi.com/1424-8220/20/5/1308
%V 20
%X Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.
@article{s20051308,
abstract = {Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system.},
added-at = {2021-06-23T10:44:14.000+0200},
article-number = {1308},
author = {Jenadeleh, Mohsen and Pedersen, Marius and Saupe, Dietmar},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/272e18e5d42a7dc158465f3c03070e517/sfbtrr161},
doi = {10.3390/s20051308},
interhash = {2742c568b25710161664555c16660771},
intrahash = {72e18e5d42a7dc158465f3c03070e517},
issn = {1424-8220},
journal = {Sensors},
keywords = {sfbtrr161 a05 from:christinawarren 2020},
number = 5,
timestamp = {2021-06-23T08:44:14.000+0200},
title = {Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition},
url = {https://www.mdpi.com/1424-8220/20/5/1308},
volume = 20,
year = 2020
}
