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dc.contributor.authorDas, Abhijit
dc.contributor.authorPal, Umapada
dc.contributor.authorFerrer, Miguel A.
dc.contributor.authorBlumenstein, Michael
dc.contributor.authorStepec, Dejan
dc.contributor.authorRot, Peter
dc.contributor.authorEmersic, Ziga
dc.contributor.authorPeer, Peter
dc.contributor.authorStruc, Vitomir
dc.contributor.authorKumar, Aruna S. V.
dc.contributor.authorHarish, B. S.
dc.contributor.editorTerry Boult
dc.date.accessioned2018-06-04T02:20:49Z
dc.date.available2018-06-04T02:20:49Z
dc.date.issued2017
dc.identifier.doi10.1109/BTAS.2017.8272764
dc.identifier.urihttp://hdl.handle.net/10072/376296
dc.description.abstractAbstract: This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject. In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82×2) eyes. A manual segmentation mask of these images was created to baseline both tasks. Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and peri-ocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems. The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameIJCB 2017
dc.relation.ispartofconferencetitle2017 IEEE International Joint Conference on Biometrics (IJCB)
dc.relation.ispartofdatefrom2017-10-01
dc.relation.ispartofdateto2017-10-04
dc.relation.ispartoflocationDenver, Colorado, United States
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchcode080199
dc.titleSSERBC 2017: Sclera segmentation and eye recognition benchmarking competition
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorBlumenstein, Michael M.
gro.griffith.authorDas, Abhijit


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