Learning-based Resolution Enhancement of Iris Images
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.
Papers:
Junzhou Huang, Li Ma, Tieniu Tan and Yunhong Wang, ” Learning-based Resolution Enhancement of Iris Images”,
14th British Machine Vision Conference, BMVC’03, pp. 153-162, Norwich, U.K., 2003.
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