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). All the images were captured by the participant using their own smartphone through a mobile application (app) developed by the authors. 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. Each subject captured their images using the same device model. This dataset's main intra- and inter-class variability are caused by lighting variation, occlusion, specular reflection, blur, motion blur, eyeglasses, off-angle, eye-gaze, makeup, and facial expression. The authors 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). The paper contains information about images' distributions by gender, age, resolution, and other experiments' details and benchmarks. Source: A new periocular dataset collected by mobile devices in unconstrained scenarios

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.

This database offers iris images (with and without contact lenses) of the same eyes captured shortly one after another with illumination coming from two different locations. 5,796 iris images in total were acquired by the LG IrisAccess 4000 sensor from 119 subjects. This set is divided into four subsets used in the experiments: (a) 1,800 images of irises wearing regular (with dot-like pattern) textured contact lenses, as shown in Fig. 6a in the wAcv 2019 paper; (b) 864 images of irises wearing irregular (without dot-like pattern) textured contact lenses, as shown in Fig. 6b in the WACV 2019 paper; (c) 1,728 images of irises wearing clear contact lenses (without any visible pattern), and (d) 1,404 images of authentic irises without any contact.

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