CASIA IRIS DATABASE PDF

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. Introduction With the pronounced need for reliable personal identification, iris recognition has become an important enabling technology in our society. Although an iris pattern is naturally an ideal identifier, the development of a high-performance iris recognition algorithm and transferring it from research lab to practical applications is still a challenging task.

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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. Introduction With the pronounced need for reliable personal identification, iris recognition has become an important enabling technology in our society.

Although an iris pattern is naturally an ideal identifier, the development of a high-performance iris recognition algorithm and transferring it from research lab to practical applications is still a challenging task.

Automatic iris recognition has to face unpredictable variations of iris images in real-world applications. For example, recognition of iris images of poor quality, nonlinearly deformed iris images, iris images at a distance, iris images on the move, and faked iris images all are open problems in iris recognition.

A basic work to solve the problems is to design and develop a high quality iris image database including all these variations. Moreover, a novel iris image database may help identify some frontier problems in iris recognition and leads to a new generation of iris recognition technology. More than 3, users from 70 countries or regions have downloaded CASIA-Iris and much excellent work on iris recognition has been done based on these iris image databases.

Although great progress of iris recognition has been achieved since s, the rapid growth of iris recognition applications has clearly highlighted two challenges, i. Usability is the largest bottleneck of current iris recognition.

It is a trend to develop long-range iris image acquisition systems for friendly user authentication. However, iris images captured at a distance are more challenging than traditional close-up iris images. Lack of long-range iris image data in the public domain has hindered the research and development of next-generation iris recognition systems. Most current iris recognition methods have been typically evaluated on medium sized iris image databases with a few hundreds of subjects.

However, more and more large-scale iris recognition systems are deployed in real-world applications. Many new problems are met in classification and indexing of large-scale iris image databases. So scalability is another challenging issue in iris recognition. 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. 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.

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CASIA Iris

Noisy Visible Wavelength Iris Image Databases News: We are pleased to announce the availability of the first as per April, fully annotated freely available data set for supporting the research about pedestrian 1 detection; 2 tracking; 3 re-identification and 4 search methods from aerial data. As a tool to support the research on pedestrian detection, tracking, re-identification and search methods, the P-DESTRE is a multi-session dataset of videos of pedestrians in outdoor public environments, fully annotated at the frame level for: 1 ID. Each pedestrian has a unique identifier that is kept among the data acquisition sessions, which enables to use the dataset for pedestrian re-identification; 2 Bounding box. However, for the sake of accuracy, present iris recognition systems require that subjects stand close less than two meters to the imaging camera and look for a period of about three seconds until the data is captured. This cooperative behavior is required to capture images with enough quality for the recognition task. However, it simultaneously restricts the range of domains where iris recognition can be applied, especially those where the subjects cooperation is not expectable e.

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Iris Database

Natural luminosity. Heterogeneity in reflections, contrast, focus and occlusions. Frontal and off-angle from various distances. Three points of each circle have been manually marked by an operator, which are used to compute the corresponding radius and centre. We do not have control of the links to the original databases. Please do a Google or similar search to try to find the database website if the links below do not work. Here, we use a subset comprising data from 75 subjects totalling 1, iris images , for which iris and eyelids segmentation groundtruth is available.

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How I can download CASIA v 2.0 database?

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How to load CASIA Iris Version V1 database images..

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