Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment

 
: Vitek, M.; Das, A.; Pourcenoux, Yann; Missler, Alexandre; Paumier, C.; Das, S.; Ghosh, Ishita de; Lucio, Diego Rafael; Zanlorensi, Luiz Antonio; Boutros, Fadi; Damer, Naser; Grebe, Jonas Henry; Kuijper, Arjan; Hu, J.; He, Y.; Wang, C.; Liu, H.; Wang, Y.; Sun, Z.; Osorio-Roig, D.; Rathgeb, Christian; Busch, Christoph; Tapia, Juan; Valenzuela, Andrés; Zampoukis, Georgios; Tsochatzidis, Lazaros; Pratikakis, Ioannis; Nathan, Sabari; Suganya, Ramamoorthy; Mehta, V.; Dhall, Abhinav; Raja, Kiran; Gupta, G.; Khiarak, Jalil Nourmohammadi; Akbari-Shahper, Mohsen; Jaryani, Farhang; Asgari-Chenaghl, Meysam; Vyas, Ritesh; Dakshit, Sagnik; Peer, Peter; Pal, Umapada; Struc, Vitomir; Menotti, David

:

Kakadiaris, Ioannis A. (General Chairs) ; Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, Biometrics Council:
IEEE International Joint Conference on Biometrics, IJCB 2020 : 28 Sept.-1 Oct. 2020, Houston, Texas, Online
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-9186-7
ISBN: 978-1-7281-9187-4
10 pp.
International Joint Conference on Biometrics (IJCB) <2020, Online>
European Commission EC
H2020; 860813; TReSPAsS-ETN
TRaining in Secure and PrivAcy-preserving biometricS
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
ATHENE
English
Conference Paper
Fraunhofer IGD ()
ATHENE; CRISP; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); biometrics; machine learning; artificial intelligence (AI); Iris recognition

Abstract
The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.

: http://publica.fraunhofer.de/documents/N-621273.html