Learning Based Resolution Enhancement of Iris Images | Zendy

Junzhou Huang | Zendy; Li Ma | Zendy; Tieniu Tan | Zendy; Yinglong Wang | Zendy

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Learning Based Resolution Enhancement of Iris Images

Author(s) -

Junzhou Huang,

Li Ma,

Tieniu Tan,

Yinglong Wang

Publication year - 2003

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.5244/c.17.16

Subject(s) - iris recognition , iris (biosensor) , computer science , artificial intelligence , computer vision , identification (biology) , resolution (logic) , relation (database) , image resolution , pattern recognition (psychology) , high resolution , image (mathematics) , low resolution , biometrics , data mining , remote sensing , geography , botany , biology

Iris recognition is one of the most reliable personal identification methods. The potential requirement of obtaining high accuracy is that users supply iris images with good quality. It is thus necessary for an iris recognition system to operate the possibly blurred iris images due to less cooperation of users and camera with low resolution. This paper proposes a new algorithm for resolution enhancement of iris images captured by the low resolution camera in less cooperative situations. The prior probability relation between the information of different frequency bands of iris features useful for recognition is firstly learned. Then, it is incorporated into resolution enhancement algorithms to recover the lost information for the seriously blurred images. A large number of experiments on the CASIA iris database demonstrate the validity of the proposed approach.

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