8th Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2023) Call for Papers

CALL FOR PARTICIPANTS - SSRBC 2023

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8th Sclera Segmentation and Recognition  Benchmarking Competition (SSRBC 2023)

Held in conjunction with IEEE/IAPR IJCB 2023
https://ijcb2023.ieee-biometrics.org/

Important dates: Registration is already open
SSRBC 2023 Website: 
https://sites.google.com/hyderabad.bits-pilani.ac.in/ssrbc2023/home
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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.