Introduction Iris is considered one of the most accurate and reliable biometric modality. Iris is more stable and distinctive compared with fingerprint, face, voice, etc, and difficult to be replicated for spoof attacks. Although an iris pattern is naturally an ideal identifier, the development of a high-performance iris recognition algorithm and transferring it from laboratory to field application is still a challenging task. In practical applications, the iris recognition system must face various unpredictable iris image degraded. For example, recognition of low-quality iris images, non-cooperative iris images, long-range iris images, and moving iris images are all huge problems in iris recognition. We believe that the first step in solving these problems is to design and develop a database of iris images that includes all of these degraded. # Brief Descriptions and Statistics of the Database CASIA-Iris-Complex contains 22,932 images from 292 Asian subjects. It includes two subsets: CASIA-Iris-CX1 and CASIA-Iris-CX2. All images were collected under NIR illumination and two eyes were captured simultaneously.

The UBIRIS.v2 iris dataset contains 11,102 iris images from 261 subjects with 10 images each subject. The images were captured under unconstrained conditions (at-a-distance, on-the-move and on the visible wavelength), with realistic noise factors. Source: Constrained Design of Deep Iris Networks Image Source: https://arxiv.org/pdf/1905.09481.pdf