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.



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.