SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition

Das, A; Pal, U; Ferrer, MA; Blumenstein, M; Štepec, D; Rot, P; Emeršič, Ž; Peer, P; Štruc, V; Kumar, SVA; Harish, BS

SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition

Das, A Pal, U Ferrer, MA Blumenstein, M

Štepec, D Rot, P Emeršič, Ž Peer, P Štruc, V Kumar, SVA Harish, BS

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Publication Type:: Conference Proceeding
Citation:: IEEE International Joint Conference on Biometrics, IJCB 2017, 2018, 2018-January pp. 742 - 747
Issue Date:: 2018-01-29

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Field	Value	Language
dc.contributor.author	Das, A	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Ferrer, MA	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	Štepec, D	en_US
dc.contributor.author	Rot, P	en_US
dc.contributor.author	Emeršič, Ž	en_US
dc.contributor.author	Peer, P	en_US
dc.contributor.author	Štruc, V	en_US
dc.contributor.author	Kumar, SVA	en_US
dc.contributor.author	Harish, BS	en_US
dc.date.available	2020-05-25T19:05:13Z
dc.date.issued	2018-01-29	en_US
dc.identifier.citation	IEEE International Joint Conference on Biometrics, IJCB 2017, 2018, 2018-January pp. 742 - 747	en_US
dc.identifier.isbn	9781538611241	en_US
dc.identifier.uri	http://hdl.handle.net/10453/126836
dc.description.abstract	© 2017 IEEE. 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.	en_US
dc.relation.ispartof	IEEE International Joint Conference on Biometrics, IJCB 2017	en_US
dc.relation.isbasedon	10.1109/BTAS.2017.8272764	en_US
dc.title	SSERBC 2017: Sclera segmentation and eye recognition benchmarking competition	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
pubs.embargo.period	Not known	en_US
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 - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. 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.

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http://hdl.handle.net/10453/126836