Iris Super Resolution Dataset
Iris recognition has been an interesting subject for many research studies in the last two decades and has raised many challenges for the researchers. One new and interesting challenge in the iris studies is gender recognition using iris images. Gender classification can be applied to reduce processing time of the identification process. On the other hand, it can be used in applications such as access control systems, and gender-based marketing and so on. To the best of our knowledge, only a few numbers of studies are conducted on gender recognition through analysis of iris images. Considering the importance of this research area and its commercial applications, it is highly essential for researchers to make use of efficient color features in their algorithms which necessitates the production of color iris image databases. The present work introduces an iris image database for gender classification. The database consists of iris images taken from 704 subjects including 392 females and 312 males in university students. For each student, more than 6 images were taken from his/her both left and right eyes. After examining the images, 3 images from the left eye and 3 images from the right eye were selected among the most appropriate images and were included in the database. All 4320 images from this database were taken under the same condition and by the same color camera.
This database contains a metadata file that specifies gender of each subject. Therefore, this database is suitable for gender classification research
Aryanmehr, S., Boroujeni, F.Z. Efficient deep CNN-based gender classification using Iris wavelet scattering. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-14062-w
Saeed Aryanmehr, Mohsen Karimi, and Farsad Zamani Boroujeni. "CVBL IRIS Gender Classification Database Image Processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL)." 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). IEEE, 2018.