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

Jenadeleh, Mohsen; Pedersen, Marius; Saupe, Dietmar

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

Dateien

Jenadeleh_2-lyzex08wpkja6.pdfGröße: 7.59 MBDownloads: 312

Datum

2020

Autor:innen

Jenadeleh, Mohsen

Pedersen, Marius

Saupe, Dietmar

Open Access-Veröffentlichung

Open Access Gold

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Zeitschriftenartikel

Publikationsstatus

Published

Erschienen in

Sensors. MDPI. 2020, 20(5), 1308. eISSN 1424-8220. Available under: doi: 10.3390/s20051308

Zusammenfassung

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.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

biometric recognition; visible light iris images; image quality assessment; image covariates; quality filtering

Zitieren

ISO 690

JENADELEH, 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/s20051308

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}
}

RDF

<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/48919">
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/48919/1/Jenadeleh_2-lyzex08wpkja6.pdf"/>
    <dc:language>eng</dc:language>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-03-04T07:51:40Z</dc:date>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/48919/1/Jenadeleh_2-lyzex08wpkja6.pdf"/>
    <dc:creator>Pedersen, Marius</dc:creator>
    <dc:rights>Attribution 4.0 International</dc:rights>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-03-04T07:51:40Z</dcterms:available>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Jenadeleh, Mohsen</dc:contributor>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:issued>2020-03</dcterms:issued>
    <dc:creator>Saupe, Dietmar</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/48919"/>
    <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dcterms:title>Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition</dcterms:title>
    <dc:creator>Jenadeleh, Mohsen</dc:creator>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:contributor>Pedersen, Marius</dc:contributor>
    <dc:contributor>Saupe, Dietmar</dc:contributor>
    <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>
  </rdf:Description>
</rdf:RDF>

Universitätsbibliographie

Ja

Begutachtet

Ja