Aufgrund von Vorbereitungen auf eine neue Version von KOPS, können kommenden Montag und Dienstag keine Publikationen eingereicht werden. (Due to preparations for a new version of KOPS, no publications can be submitted next Monday and Tuesday.)
Type of Publication: | Contribution to a conference collection |
Publication status: | Published |
URI (citable link): | http://nbn-resolving.de/urn:nbn:de:bsz:352-2-1db8odgkzm0gn5 |
Author: | Jenadeleh, Mohsen; Pedersen, Marius; Saupe, Dietmar |
Year of publication: | 2018 |
Conference: | CVPR 2018 : IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 18, 2018 - Jun 22, 2018, Salt Lake City, Utah, USA |
Published in: | The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. - Computer Vision Foundation, 2018. - pp. 556-565 |
URL of original publication: | http://openaccess.thecvf.com/content_CVPR_2018/papers/w11/Jenadeleh_Realtime_Quality_Assessment_CVPR_2018_paper.pdf, Last access on Jun 14, 2018 |
DOI (citable link): | https://dx.doi.org/10.1109/CVPRW.2018.00085 |
Summary: |
Ensuring sufficient quality of iris images acquired by handheld imaging devices in visible light poses many challenges to iris recognition systems. Many distortions affect the input iris images, and the source and types of these distortions are unknown in uncontrolled environments. We propose a fast no-reference image quality assessment measure for predicting iris image quality to handle severely degraded iris images. The proposed differential sign-magnitude statistics index (DSMI) is based on statistical features of the local difference sign-magnitude transform, which are computed by comparing the local mean with the central pixel of the patch and considering the noticeable variations. The experiments, conducted with a reference iris recognition system and three visible light datasets, showed that the quality of iris images strongly affects the recognition performance. Using the proposed method as a quality filtering step improved the performance of the iris recognition system by rejecting poor quality iris samples.
|
Subject (DDC): | 004 Computer Science |
Link to License: | In Copyright |
Bibliography of Konstanz: | Yes |
JENADELEH, Mohsen, Marius PEDERSEN, Dietmar SAUPE, 2018. Realtime Quality Assessment of Iris Biometrics under Visible Light. CVPR 2018 : IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, Utah, USA, Jun 18, 2018 - Jun 22, 2018. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops. Computer Vision Foundation, pp. 556-565. Available under: doi: 10.1109/CVPRW.2018.00085
@inproceedings{Jenadeleh2018Realt-42587, title={Realtime Quality Assessment of Iris Biometrics under Visible Light}, url={http://openaccess.thecvf.com/content_CVPR_2018/papers/w11/Jenadeleh_Realtime_Quality_Assessment_CVPR_2018_paper.pdf}, year={2018}, doi={10.1109/CVPRW.2018.00085}, publisher={Computer Vision Foundation}, booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, pages={556--565}, author={Jenadeleh, Mohsen and Pedersen, Marius and Saupe, Dietmar} }
<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/rdf/resource/123456789/42587"> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/42587"/> <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/> <dc:language>eng</dc:language> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/42587/3/Jenadeleh_2-1db8odgkzm0gn5.pdf"/> <dcterms:title>Realtime Quality Assessment of Iris Biometrics under Visible Light</dcterms:title> <dc:contributor>Jenadeleh, Mohsen</dc:contributor> <dc:contributor>Pedersen, Marius</dc:contributor> <dcterms:issued>2018</dcterms:issued> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-06-14T12:59:46Z</dcterms:available> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-06-14T12:59:46Z</dc:date> <dc:contributor>Saupe, Dietmar</dc:contributor> <dc:rights>terms-of-use</dc:rights> <dc:creator>Saupe, Dietmar</dc:creator> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/42587/3/Jenadeleh_2-1db8odgkzm0gn5.pdf"/> <dc:creator>Pedersen, Marius</dc:creator> <dcterms:abstract xml:lang="eng">Ensuring sufficient quality of iris images acquired by handheld imaging devices in visible light poses many challenges to iris recognition systems. Many distortions affect the input iris images, and the source and types of these distortions are unknown in uncontrolled environments. We propose a fast no-reference image quality assessment measure for predicting iris image quality to handle severely degraded iris images. The proposed differential sign-magnitude statistics index (DSMI) is based on statistical features of the local difference sign-magnitude transform, which are computed by comparing the local mean with the central pixel of the patch and considering the noticeable variations. The experiments, conducted with a reference iris recognition system and three visible light datasets, showed that the quality of iris images strongly affects the recognition performance. Using the proposed method as a quality filtering step improved the performance of the iris recognition system by rejecting poor quality iris samples.</dcterms:abstract> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:creator>Jenadeleh, Mohsen</dc:creator> </rdf:Description> </rdf:RDF>
Jenadeleh_2-1db8odgkzm0gn5.pdf | 420 |