Prentice-Hall, Englewood Cliffs (1982)Ĭanny, J.: A computational approach to edge detection. Brief Descriptions and Statistics of the DatabaseBallard, D.H., Brown, C.M.: Computer Vision. 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. 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. In practical applications, the iris recognition system must face various unpredictable iris image degraded. 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. Iris is more stable and distinctive compared with fingerprint, face, voice, etc, and difficult to be replicated for spoof attacks. Iris is considered one of the most accurate and reliable biometric modality. All images were collected under NIR illumination and two eyes were captured simultaneously. It includes two subsets: CASIA-Iris-CX1 and CASIA-Iris-CX2. # Brief Descriptions and Statistics of the DatabaseĬASIA-Iris-Complex contains 22,932 images from 292 Asian subjects.
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