The UFPR-Eyeglasses dataset has 1,135 images of both eyes (2,270 cropped images of each eye) from 83 subjects (166 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. This dataset contains some images from the UFPR-Periocular dataset and has samples from all subjects wearing and not wearing eyeglasses.
We manually annotated the iris bounding boxes and used it to normalize the images regarding scale and rotation. The intra-class variations are mainly caused by illumination, occlusions, distances, reflection, eyeglasses, and image quality.
All the original, cropped periocular images and iris bounding box annotations are publicly available for the research community (upon request).
How to obtain the Dataset
The UFPR-Eyeglasses 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 (menotti@inf.ufpr.br). 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.
References
If you use the UFPR-Eyeglasses dataset in your research please cite our paper:
- L. A. Zanlorensi, H. Proença, and D. Menotti, “Unconstrained Periocular Recognition: Using Generative Deep Learning Frameworks for Attribute Normalization” in 2020 IEEE International Conference on Image Processing (ICIP), October 2020, pp. 1361-1365. [IEEE Xplore] [PDF] [BibTeX]
You may also be interested in our larger dataset with similar conditions:
- 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]
Contact
Please contact Luiz A. Zanlorensi (lazjunior@inf.ufpr.br) with questions or comments.