9th Sclera Segmentation and Benchmarking Competition (SSBC 2025)

How to participate?

Registration for the competition can be done via email. If you would like to register and receive the training dataset and starter kit, please send an email to abhijit.das@hyderabad.bits-pilani.ac.in and matej.vitek@fri.uni-lj.si with the subject line as "SSBC 2025 registration" with the following information:

Name, Affiliation, Email, Phone number, CV , and Mailing Address.

Execution

In this competition, we aim to benchmark the performance of sclera segmentation models trained with synthetic ocular images and corresponding automatically generated (weak) ground truth segmentation masks. The primary motivation behind this challenge is to push the boundaries of current machine learning techniques in handling ocular biometrics, particularly under conditions where traditional, manually annotated datasets are scarce or unavailable. By focusing on synthetic images and weakly labelled data, we hope to stimulate research into more robust and generalized machine learning models that can perform accurately even when faced with imperfect or limited training data. This approach not only has the potential to significantly reduce the time and cost associated with manual annotation but also paves the way for advancements in areas where high-quality, real-world data is hard to come by. Through this competition, we aim to foster a collaborative effort within the research community to address these challenges, ultimately contributing to more effective and accessible biometric identification technologies. 

The competition will address the problem of sclera segmentation, where participants will have to learn their models on: (1) synthetic data only and (2) a combination of real and synthetic data. For the evaluation, participants will have to provide results on: 

• Segmentation with synthetic data: for this task, participants will have to learn segmentation models on two sets of training data: (1) synthetic training data only, and (2) a combination of real and synthetic data, and then test the learned models on a synthetic test dataset. Here, the synthetic test data will consist of images from two data generators (one producing Caucasian and the other Asian ocular images). Segmentation masks generated on the test data will have to be submitted for scoring. Additionally, participant will also have to submit complete models and training scripts, so the organizers can validate the results. 

• Segmentation with real data: for this task, participants will have to learn segmentation models on two sets of training data: (1) synthetic training data only, and (2) a combination of real and synthetic data, and then test the learned models on the real-world datasets. In this case, manually generated (ground truth) segmentation masks will be used for generating the performance scores.

All participants, whose submission will yield competitive performance (an F1>0.7 for one of the segmentation tasks), will be invited to be co-authors on the summary paper of the competition. The summary paper will be submitted to IEEE IJCB 2025 and if accepted will appear in IEEE Xplore.

A detailed document with instructions on the competition is available HERE.

Organizing Institutions

Relevant Publications

• M. Vitek, A.Das et al., "Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach," in IEEE Transactions on Information Forensics and Security, vol. 18, pp. 190-205, 2023. [PDF]

• D. Tomašević; P. Peer, V. Štruc, BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images, IJCB 2022, pp. 1-10, 2022. [PDF]

• M. Vitek, A. Das et al. , SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment, IJCB 2020. [PDF]

• A. Das, U Pal, M. Blumenstein, C. Wang, Y. He, Y. Zhu, Z. Sun, Sclera Segmentation Benchmarking Competition in Cross-resolution Environment, ICB 2019. [PDF]