SSBC 2020: sclera segmentation benchmarking competition in the mobile environment
Vitek, M.; Das, A.; Pourcenoux, Y.; Missler, A.; Paumier, C.; Das, S.; Ghosh, I. D.; Lucio, D. R.; Zanlorensi Jr, L. A.; Menotti, D.; Boutros, F.; Damer, N.; Grebe, J. H.; Kuijper, A.; Hu, J.; He, Y.; Wang, C.; Liu, H.; Wang, Y.; Sun, Z.; Osorio-Roig, D.; Rathgeb, C.; Busch, C.; Tapia, J.; Valenzuela, A.; Zampoukis, G.; Tsochatzidis, L.; Pratikakis, I.; Nathan, S.; Suganya, R.; Mehta, V.; Dhall, A.; Raja, K.; Gupta, G.; Khiarak, J. N.; Akbari-Shahper, M.; Jaryani, F.; Asgari-Chenaghlu, M.; Vyas, R.; Dakshit, S.; Peer, P.; Pal, U.; Struc, V.
Date:
2021-06-11
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
groups 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.
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