CASIA-IrisV4 is an extension of CASIA-IrisV3 and contains six subsets. The three subsets from CASIA-IrisV3 are CASIA-Iris-Interval, CASIA-Iris-Lamp, and CASIA-Iris-Twins respectively. The three new subsets are CASIA-Iris-Distance, CASIA-Iris-Thousand, and CASIA-Iris-Syn.

CASIA-IrisV4 contains a total of 54,601 iris images from more than 1,800 genuine subjects and 1,000 virtual subjects. All iris images are 8 bit gray-level JPEG files, collected under near infrared illumination or synthesized. Some statistics and features of each subset are given in Table 1. The six data sets were collected or synthesized at different times and CASIA-Iris-Interval, CASIA-Iris-Lamp, CASIA-Iris-Distance, CASIA-Iris-Thousand may have a small inter-subset overlap in subjects.

Data Collection

2.1 CASIA-Iris-Interval

Iris images of CASIA-Iris-Interval were captured with our self-developed close-up iris camera (Fig.1). The most compelling feature of our iris camera is that we have designed a circular NIR LED array, with suitable luminous flux for iris imaging. Because of this novel design, our iris camera can capture very clear iris images (see Fig.2). CASIA-Iris-Interval is well-suited for studying the detailed texture features of iris images.

2.2 CASIA-Iris-Lamp

CASIA-Iris-Lamp was collected using a hand-held iris sensor produced by OKI (Fig.3). A lamp was turned on/off close to the subject to introduce more intra-class variations when we collected CASIA-Iris-Lamp. Elastic deformation of iris texture (Fig.4) due to pupil expansion and contraction under different illumination conditions is one of the most common and challenging issues in iris recognition. So CASIA-Iris-Lamp is good for studying problems of non-linear iris normalization and robust iris feature representation.

2.3 CASIA-Iris-Twins

CASIA-Iris-Twins contains iris images of 100 pairs of twins, which were collected during Annual Twins Festival in Beijing using OKI's IRISPASS-h camera (Fig.5). Although iris is usually regarded as a kind of phenotypic biometric characteristics and even twins have their unique iris patterns, it is interesting to study the dissimilarity and similarity between iris images of twins.

2.4 CASIA-Iris-Distance

CASIA-Iris-Distance contains iris images captured using our self-developed long-range multi-modal biometric image acquisition and recognition system (LMBS, Fig.6). The advanced biometric sensor can recognize users from 3 meters away by actively searching iris, face or palmprint patterns in the visual field via an intelligent multi-camera imaging system. The LMBS is human-oriented by fusing computer vision, human computer interaction and multi-camera coordination technologies and improves greatly the usability of current biometric systems. The iris images of CASIA-Iris-Distance were captured by a high resolution camera so both dual-eye iris and face patterns are included in the image region of interest (Fig. 7).And detailed facial features such as skin pattern are also visible for multi-modal biometric information fusion.

2.5 CASIA-Iris-Thousand

CASIA-Iris-Thousand contains 20,000 iris images from 1,000 subjects, which were collected using IKEMB-100 camera (Fig. 8) produced by IrisKing. IKEMB-100 is a dual-eye iris camera with friendly visual feedback, realizing the effect of ¡°What You See Is What You Get¡±. The bounding boxes shown in the frontal LCD help users adjust their pose for high-quality iris image acquisition. The main sources of intra-class variations in CASIA-Iris-Thousand are eyeglasses and specular reflections. Since CASIA-Iris-Thousand is the first publicly available iris dataset with one thousand subjects, it is well-suited for studying the uniqueness of iris features and develop novel iris classification and indexing methods.

2.6 CASIA-Iris-Syn

CASIA-Iris-Syn contains 10,000 synthesized iris images of 1,000 classes. The iris textures of these images are synthesized automatically from a subset of CASIA-IrisV1 with the approach described in [1] (Fig. 10). Then the iris ring regions were embedded into the real iris images, which makes the artificial iris images more realistic. The intra-class variations introduced into the synthesized iris dataset include deformation, blurring, and rotation, which raise a challenge problem for iris feature representation and matching. We have demonstrated in [1] that the synthesized iris images are visually realistic and most subjects can not distinguish genuine and artificial iris images. More importantly, the performance results tested on the synthesized iris image database have similar statistical characteristics to genuine iris database. So users of CASIA-IrisV4 are encouraged to use CASIA-Iris-Syn for iris recognition research and any suggestions are welcome. If CASIA-Iris-Syn proves to be successful for most researchers of iris recognition, we will provide more and more synthesized iris images in the future.