Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition

Kurzanzeige
dc.contributor.authorJenadeleh, Mohsen
dc.contributor.authorPedersen, Marius
dc.contributor.authorSaupe, Dietmar
dc.date.accessioned2020-03-04T07:51:40Z
dc.date.available2020-03-04T07:51:40Z
dc.date.issued2020-03eng
dc.description.abstractImage 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.eng
dc.description.versionpublishedeng
dc.identifier.doi10.3390/s20051308eng
dc.identifier.ppn1691499463
dc.identifier.urihttps://kops.uni-konstanz.de/handle/123456789/48919
dc.language.isoengeng
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectbiometric recognition; visible light iris images; image quality assessment; image covariates; quality filteringeng
dc.subject.ddc004eng
dc.titleBlind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognitioneng
dc.typeJOURNAL_ARTICLEeng
dspace.entity.typePublication
kops.citation.bibtex
@article{Jenadeleh2020-03Blind-48919,
  year={2020},
  doi={10.3390/s20051308},
  title={Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition},
  number={5},
  volume={20},
  journal={Sensors},
  author={Jenadeleh, Mohsen and Pedersen, Marius and Saupe, Dietmar},
  note={Article Number: 1308}
}
kops.citation.iso690JENADELEH, Mohsen, Marius PEDERSEN, Dietmar SAUPE, 2020. Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition. In: Sensors. MDPI. 2020, 20(5), 1308. eISSN 1424-8220. Available under: doi: 10.3390/s20051308deu
kops.citation.iso690JENADELEH, Mohsen, Marius PEDERSEN, Dietmar SAUPE, 2020. Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition. In: Sensors. MDPI. 2020, 20(5), 1308. eISSN 1424-8220. Available under: doi: 10.3390/s20051308eng
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    <dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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