| Author | Das, Abhijit; Atreya, Saurabh; Mukherjee, Aritra; Vitek, Matej; Haiqing, Li; Wang, Caiyong; Zhao, Guangzhe; Boutros, Fadi; Siebke, Patrick; Kolf, Jan Niklas; Damer, Naser; Sun, Ye; Lu, Hexin; Aobo, Fan; Sheng, You; Nathan , Sabari; Suganya, R.; Rampriya, R.S.; Sharma, Geetanjali; Priyanka, P.; Nigam, Aditya; Peer, Peter; Pal, Umapada; Štruc, Vitomir |
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| Abstract | This paper presents the summary of the Sclera Segmentation and Joint Recognition Benchmarking Competition (SSRBC 2023) held in conjunction with IEEE International Joint Conference on Biometrics (IJCB 2023). Different from the previous editions of the competition, SSRBC 2023 not only explored the performance of the latest and most advanced sclera segmentation models, but also studied the impact of segmentation quality on recognition performance. Five groups took part in SSRBC 2023 and submitted a total of six segmentation models and one recognition technique for scoring. The submitted solutions included a wide variety of conceptually diverse deep-learning models and were rigorously tested on three publicly available datasets, i.e., MASD, SBVPI and MOBIUS. Most of the segmentation models achieved encouraging segmentation and recognition performance. Most importantly, we observed that better segmentation results always translate into better verification performance. |
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