SSRBC 2016: Sclera Segmentation and Recognition Benchmarking Competition

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

SSRBC 2016: Sclera Segmentation and Recognition Benchmarking Competition

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

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Publication Type:: Conference Proceeding
Citation:: 2016 International Conference on Biometrics, ICB 2016, 2016
Issue Date:: 2016-08-23

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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.date.issued	2016-08-23	en_US
dc.identifier.citation	2016 International Conference on Biometrics, ICB 2016, 2016	en_US
dc.identifier.isbn	9781509018697	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122983
dc.description.abstract	© 2016 IEEE. This article reports and summarizes the results of a competition on sclera segmentation and recognition benchmarking, called Sclera Segmentation and Recognition Benchmarking Competition 2016 (SSRBC 2016). It was organized in the context of the 9th IAPR International Conference on Biometrics (ICB 2016). The goal of this competition was to record the recent developments in sclera segmentation and recognition, and also to gain the attention of researchers on this subject of biometrics. In this regard, we have used a multi-angle sclera dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82∗2) different eyes. We have prepared a manual segmentation mask of these images to create the baseline for both tasks. We have, furthermore, adopted precision and recall based statistical measures to evaluate the effectiveness of the segmentation and the ranks of the competing algorithms. The recognition accuracy measure has been employed to measure the recognition task. To summarize, twelve participants registered for the competition, and among them, three participants submitted their algorithms/ systems for the segmentation task and two their recognition algorithm. The results produced by these algorithms reflect developments in the literature of sclera segmentation and recognition, employing cutting edge segmentation techniques. Along with the algorithms of three competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website. The competition also demonstrates the recent interests of researchers from academia as well as industry on this subject of biometrics.	en_US
dc.relation.ispartof	2016 International Conference on Biometrics, ICB 2016	en_US
dc.relation.isbasedon	10.1109/ICB.2016.7550069	en_US
dc.title	SSRBC 2016: Sclera Segmentation and Recognition Benchmarking Competition	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	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

Abstract:

© 2016 IEEE. This article reports and summarizes the results of a competition on sclera segmentation and recognition benchmarking, called Sclera Segmentation and Recognition Benchmarking Competition 2016 (SSRBC 2016). It was organized in the context of the 9th IAPR International Conference on Biometrics (ICB 2016). The goal of this competition was to record the recent developments in sclera segmentation and recognition, and also to gain the attention of researchers on this subject of biometrics. In this regard, we have used a multi-angle sclera dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82∗2) different eyes. We have prepared a manual segmentation mask of these images to create the baseline for both tasks. We have, furthermore, adopted precision and recall based statistical measures to evaluate the effectiveness of the segmentation and the ranks of the competing algorithms. The recognition accuracy measure has been employed to measure the recognition task. To summarize, twelve participants registered for the competition, and among them, three participants submitted their algorithms/ systems for the segmentation task and two their recognition algorithm. The results produced by these algorithms reflect developments in the literature of sclera segmentation and recognition, employing cutting edge segmentation techniques. Along with the algorithms of three competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website. The competition also demonstrates the recent interests of researchers from academia as well as industry on this subject of biometrics.

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http://hdl.handle.net/10453/122983