Abstract:
There is a strong correlation between individuals and their iris patterns and those
patterns are unchanged throughout human life. Therefore, the iris biometric is
regarded as a distinct, reliable biometric that can be utilized as an authentication
element. The privacy of users and the security of iris biometric systems can be
seriously threatened if attackers gain access to users' enrolled biometric information.
The recognition accuracy and privacy of enrolled iris templates of an iris recognition
system are two essential aspects required to maintain at a higher level. Information
distortion is one of the convenient ways to provide privacy on an iris template.
However, this would result in degrading the recognition accuracy of the iris
recognition system. When an authentication system tries to provide both
concurrently, there is a trade-off between recognition accuracy and privacy aspects.
It would be a significant result if the research could make a well-balanced trade-off
between the recognition accuracy and privacy of iris templates. Transforming iris
features is one strategy to achieve privacy in iris templates. We propose an approach
that processes the features of an iris template block-wise. However, the block size is
limited to the size of a column to control the degradation of discriminatory
information of original iris templates. The XOR operation is applied to three adjacent
columns in two steps in the fusion process. In this process, the first XOR operation is
applied between a column and its’ next adjoining column. The second XOR operation
is applied between the previous result and the next adjacent column. This process
continues up to the end of the input iris template. Two datasets were used to test the
three proposed approaches. The proposed approach achieved higher recognition
accuracy meantime keeping privacy at an acceptable status. Based on the results of
dataset 1, the proposed approach accepts genuine users at a rate of 99.11% while it
accepts 0.01% of imposters. For dataset 2, the Genuine Acceptance Rate (GAR) is
depicted as 81.12% while FAR is at 0.01%. As further improvements, the research
can be extended to more widespread databases and higher-quality iris samples.
