Sclera Segmentation Benchmarking Competition in Cross-resolution Environment

Das, A; Pal, U; Blumenstein, M; Wang, C; He, Y; Zhu, Y; Sun, Z

Sclera Segmentation Benchmarking Competition in Cross-resolution Environment

Das, A Pal, U Blumenstein, M

Wang, C He, Y Zhu, Y Sun, Z

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 International Conference on Biometrics, ICB 2019, 2020, 00, pp. 1-7
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Das, A
dc.contributor.author	Pal, U
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744
dc.contributor.author	Wang, C
dc.contributor.author	He, Y
dc.contributor.author	Zhu, Y
dc.contributor.author	Sun, Z
dc.date	2019-06-04
dc.date.accessioned	2021-05-19T05:31:52Z
dc.date.available	2021-05-19T05:31:52Z
dc.date.issued	2020
dc.identifier.citation	2019 International Conference on Biometrics, ICB 2019, 2020, 00, pp. 1-7
dc.identifier.isbn	9781728136400
dc.identifier.issn	2376-4201
dc.identifier.uri	http://hdl.handle.net/10453/148965
dc.description.abstract	© 2019 IEEE. This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research.For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using.a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed.Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 International Conference on Biometrics, ICB 2019
dc.relation.ispartof	International Conference on Biometrics
dc.relation.isbasedon	10.1109/ICB45273.2019.8987414
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Sclera Segmentation Benchmarking Competition in Cross-resolution Environment
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Crete, Greece
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-19T05:31:49Z
pubs.finish-date	2019-06-07
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-06-04
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. This paper summarizes the results of the Sclera Segmentation Benchmarking Competition (SSBC 2019). It was organized in the context of the 12th IAPR International Conference on Biometrics (ICB 2019). The aim of this competition was to record the developments on sclera segmentation in the cross-resolution environment (sclera trait captured using multiple acquiring sensors with different image resolutions). Additionally, the competition also aimed to gain the attention of researchers on this subject of research.For the purpose of benchmarking, we have employed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1). The second dataset is the Mobile Sclera Dataset (MSD), and in this dataset, images were captured using.a mobile phone rear camera of 8-megapixels. Baseline manual segmentation masks of the sclera images from both the datasets were developed.Precision and recall-based measures were employed to evaluate the effectiveness and ranking of the submitted segmentation techniques. Four algorithms were submitted to address the segmentation task. In this paper we analyzed the results produced by these algorithms/systems, and we have defined a way forward for this problem. Both the datasets along with some of the accompanying ground truth/baseline masks will be freely available for research purposes.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148965