Vitek, M. ; Das, A. ; Pourcenoux, Y. ; Missler, A. ; Paumier, C. ; Das, S. ; De Ghosh, I. ; Lucio, D. R. ; Zanlorensi, 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. (2020)
SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment.
2020 IEEE International Joint Conference on Biometrics (IJCB). virtual Conference (28.09.-01.10.2020)
doi: 10.1109/IJCB48548.2020.9304881
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Vitek, M. ; Das, A. ; Pourcenoux, Y. ; Missler, A. ; Paumier, C. ; Das, S. ; De Ghosh, I. ; Lucio, D. R. ; Zanlorensi, 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. |
| Art des Eintrags: | Bibliographie |
| Titel: | SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Verlag: | IEEE |
| Veranstaltungstitel: | 2020 IEEE International Joint Conference on Biometrics (IJCB) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 28.09.-01.10.2020 |
| DOI: | 10.1109/IJCB48548.2020.9304881 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Machine learning, Artificial intelligence (AI), Iris recognition |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 01 Feb 2021 08:11 |
| Letzte Änderung: | 01 Feb 2021 08:11 |
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