The UFPR-Periocular dataset has 16,830 images of both eyes (33,660 cropped images of each eye) from 1,122 subjects (2,244 classes). It has been introduced in our paper [PDF].
All the images were captured by the participant using their own smartphone through a mobile application (app) developed by us.
There are 15 samples from each subject’s eye, obtained in 3 sessions (5 images per session) with a minimum interval of 8 hours between the sessions.
The images were collected from June 2019 to January 2020 and have several resolutions varying from 360×160 to 1862×1008 pixels – depending on the mobile device used to capture the image. In total, the dataset has images captured from 196 different mobile devices.
We remark that each subject captured all of their images using the same device model. The main intra- and inter-class variability in this dataset is caused by lighting variation, occlusion, specular reflection, blur, motion blur, eyeglasses, off-angle, eye-gaze, makeup, and facial expression.
We manually annotated the eye corner of all images with 4 points (inside and outside eye corners) and used it to normalize the periocular region regarding scale and rotation. All the original and cropped periocular images, eye corner annotations, and experimental protocol files are publicly available for the research community (upon request).
Infos about images’ distributions by gender, age, resolution, and other experiments’ details, and benchmark are in our paper.
How to obtain the Dataset
The UFPR-Periocular dataset is released only to academic researchers from educational or research institutes for non-commercial purposes.
To be able to download the dataset, please read carefully this license agreement, fill it out and send it back to Professor David Menotti (email@example.com). The license agreement MUST be reviewed and signed by the individual or entity authorized to make legal commitments on behalf of the institution or corporation (e.g., Department/Administrative Head, or similar). We cannot accept licenses signed by students or faculty members.
If you use the UFPR-Periocular dataset in your research please cite our paper:
- L. A. Zanlorensi, R. Laroca, D. R. Lucio, L. R. Santos, A. S. Britto Jr., and D. Menotti, “A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios,” Scientific Reports, vol. 12, p. 17989, 2022. [Nature] [arXiv] [PDF] [BibTeX]
Please contact Luiz A. Zanlorensi (firstname.lastname@example.org) with questions or comments.