Sclera biometrics have gained significant popularity among emerging ocular traits in the last few years. In order to evaluate the potential of this trait, a considerable amount of research has been presented in the literature, both employing the sclera individually and in combination with the iris. In spite of those initiatives, sclera biometrics need to be studied more extensively to ascertain their usefulness. Moreover, the sclera segmentation task still requires a significant amount of attention due to challenges associated with the performance of existing techniques while sclera recognition is performed in cross-sensor and resolution scenarios. In order to investigate these challenges, document recent development and attract the attention/interest of researchers we are planning to host the next Sclera Segmentation and Recognition Benchmarking Competition SSRBC 2023. SSRBC 2023 will be the 7 th in the series of sclera (segmentation and recognition) benchmarking competitions following SSBC 2015, SSRBC 2016, SSERBC 2017, SSBC 2018, SSBC 2019 and SSBC 2020 held in conjunction with BTAS 2015, ICB 2016, IJCB 2017, ICB 2018, 19 and 20, respectively. Due to the overwhelming success of SSBC 2015, SSRBC 2016, SSERBC 2017, SSBC 2018, 2019 and IJCB 2020, we plan to organize this proposed competition to benchmark sclera segmentation and recognition jointly with both cross-sensor and low and high-resolution images.

How to participate?

Registration for the competition can be done by email. If you would like to register and receive the training dataset, please send an email to abhijit.das@hyderabad.bits-pilani.ac.in with the subject line as “SSRBC 2023 registration” with the following information:

Name, Affiliation, Email, Phone number, CV , Mailing Address and signed version of the following form .

Organizers :

Dr. Abhijit Das, BITS Pilani, Hyderabad, India  (abhijit.das@hyderabad.bits-pilani.ac.in)

Dr. Aritra Mukherjee, BITS Pilani, , Hyderabad, India  (a.mukherjee@hyderabad.bits-pilani.ac.in)

Prof. Umapada Pal,  Indian Statistical Institute, Kolkata, India (umapada@isical.ac.in )

Prof. Peter Peer, University of Ljubljana, Ljubljana, Slovenija (peter.peer @fri.uni-lj.si)

Assoc. Prof. Vitomir Štruc , University of Ljubljana, Ljubljana, Slovenija (vitomir.struc @fe.uni-lj.si)

Execution

Description of the dataset(s) used for the competition and the available annotations

The competition aims to benchmark the sclera segmentation and recognition tasks with a dataset containing both low and high-resolution images. Three different datasets will be employed for the competition, where two were acquired with a DSLR camera and one by a mobile camera.

The first dataset, i.e, the multi-angle sclera dataset (MASD), consists of 2624 RGB images taken from 82 identities. Images were collected from both the eyes of each individual, so there are 164 different eyes in total in the dataset. For each individual image, four gaze directions (looking straight, left, right and up) were captured and for each direction 4 images were taken. The subjects from the database are both male and female and with different eye colors, few of them are wearing contact lenses and images were taken at different times of the day. The database contains images with blinking eyes, closed eyes and blurred eyes. High-resolution images stored in JPEG format are provided in the database (7500 x 5000 dimensions). A NIKON D 800 camera and 28300 lenses were used for image capturing. A ground truth or manual sclera segmentation of this dataset is also available. For development purposes, a subset of the database, both eye images and ground truth (1 image for each angle/gaze of the first 30 subjects, i.e. 120 images in total) will be provided to the participants.

The second dataset, the Mobile sclera dataset (MSD), consists of 500 RGB images from both eyes of 25 individuals (in other words 50 different eyes). For each eye, 10 images were captured. The database contains blurred images and images with blinking eyes. The individuals comprise both males and females (12 males and 13 females), of different ages and different skin colors, 2 of them were wearing contact lenses and the images were taken at different times of the day. Variation in image quality (blur, lighting condition etc.) and different acquisition conditions was included intentionally in the database to investigate the performance of the framework in non-ideal scenarios. High-resolution images (3264 × 2448) of 96 dpi are included in the database. All the images are in JPEG format. The images were captured using a mobile camera with an 8-megapixel rear camera.

The third dataset, SBVPI, consists of 1858 RGB images of 110 eyes (i.e., 55 subjects) captured with a DSLR camera (specifically, a Canon EOS 60D with macro lenses). All images were manually cropped to extract the desired ROI while maintaining their aspect ratio, then rescaled to 3000 × 1700 pixels to maintain a consistent image size across the entire dataset. Images in the dataset were captured at the highest resolution and quality settings available in the camera and in a laboratory environment. The dataset contains images taken under 4 different gaze directions, with a minimum of 4 images per direction for each subject. The appearance variability in SBVPI is due to identity, eye color, gender, and age. Manually generated markups of the sclera and periocular regions are present for all images. SBVPI is publicly available for research purposes.

Details on the experimental protocol and result generation/submission procedure,

The competition will address two problems of relevance to IJCB 2023, sclera segmentation and recognition, and will be organized around three tasks:

● Segmentation task: for the segmentation task, participants will have to learn segmentation models on the MASD datasets and then test them on the MSD and SBVPI datasets. Complete algorithms will have to be submitted for scoring. The final performance evaluation will be conducted by the organizers.

● Recognition task: for the recognition task, the participants will be asked to develop recognition models on the MASD datasets and then submit the trained models for scoring to the organizers. The performance evaluation will be conducted on the sequestered MSD and SBVPI dataset. In this case, the manually generated (ground truth) segmentation mask will be used to get the ROI before subjecting the images to the recognition/feature extraction models..

● Joint segmentation and Recognition task: for the joint segmentation-recognition task, the participants will be asked to develop segmentation as well as recognition models on the MASD datasets and then submit the trained models for scoring to the organizers. The performance evaluation will be conducted on the sequestered MSD and SBVPI dataset. In this case, the segmentation masks generated by the models of the participants will be used to extract the ROI. To ensure the models are only trained on the vasculature of the sclera, the segmentation masks generated by the segmentation models will be used to remove all parts of the images that do not belong to the sclera prior to subjecting images to the recognition model/feature extractor.

Description of the evaluation criteria (performance metrics) and available baseline implementations/code (e.g., a starter kit).

● Segmentation task: The evaluation measures will be precision and recall (recall will consider the prior measure for ranking the algorithms). The ground truth of the manually segmented sclera region in an eye image is constructed, which will be used as a baseline.

● Recognition task: For the recognition task, we will consider verification experiments and report the Area Under the ROC Curve (AUC) as our main competition metric. For the summary paper, other relevant performance indicators will also be reported.

A detailed timeline for the competition:

● Site opens 14th Feb 2023

● Registration starts 14th Feb 2023

● Test dataset available 28th Feb 2023

● Registration closes 10th May 2023

● Algorithm submission deadline 10th May 2023

● Results and report announcement 15th May 2023

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, doi: 10.1109/TIFS.2022.3216468.

● V. Matej, A. Das et al. , SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment, IJCB 2020.

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