SSBC 2018: Sclera segmentation benchmarking competition

Das, A; Pal, U; Ferrer, MA; Blumenstein, M; Stepec, D; Rot, P; Emersic, Z; Peer, P; Struc, V

SSBC 2018: Sclera segmentation benchmarking competition

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

Stepec, D Rot, P Emersic, Z Peer, P Struc, V

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2018 International Conference on Biometrics, ICB 2018, 2018, pp. 303 - 308
Issue Date:: 2018-07-13

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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	Stepec, D	en_US
dc.contributor.author	Rot, P	en_US
dc.contributor.author	Emersic, Z	en_US
dc.contributor.author	Peer, P	en_US
dc.contributor.author	Struc, V	en_US
dc.date.issued	2018-07-13	en_US
dc.identifier.citation	Proceedings - 2018 International Conference on Biometrics, ICB 2018, 2018, pp. 303 - 308	en_US
dc.identifier.isbn	9781538642856	en_US
dc.identifier.uri	http://hdl.handle.net/10453/133831
dc.description.abstract	© 2018 IEEE. This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed 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), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using.a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email.	en_US
dc.relation.ispartof	Proceedings - 2018 International Conference on Biometrics, ICB 2018	en_US
dc.relation.isbasedon	10.1109/ICB2018.2018.00053	en_US
dc.title	SSBC 2018: Sclera segmentation benchmarking competition	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed 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), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using.a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email.

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http://hdl.handle.net/10453/133831