This system uses the distinctive human iris patterns to successfully identify authorized individuals. It detects the iris region from the eye image and then isolates the region and extracts useful features from it. It divides the selected iris region into pixels following a preset radial and angular and then maps the circular region into rectangular polar coordinate according to the resolutions. This polar image is then convolved with 1D log Gabor filter and phase of the response is quantized to four levels to generate the binary iris template and its corresponding mask. The Hamming distance between two iris templates is computed to find out if the templates are generated from the same iris or not. The conventional systems measure the Hamming Distance using iris images from a single eye. However, the proposed system takes images from both eyes simultaneously for comparison process which shows an increased accuracy and performance by eliminating the false acceptance (acceptance of intruders) and minimizing the false rejection (rejection of authorized personnel).
The system was tested using 1180 eye images of 118 persons from CASIA Iris database V3 provided by Chinese Academy of Sciences’ Institute of Automation (CASIA) and showed better performance than conventional systems. Using the proposed system, false accept rate and false reject rate were found to be 0% and 9.96% respectively with an overall accuracy of 99.92% whereas the conventional systems showed 99.87% accuracy and 14.62% to 15.68% false reject rate to achieve the goal of no false acceptance.
The ongoing research will enhance the security of the system at storage-level. The enhancements include:
Introducing template-encryption in the system.
Eliminating the threat of biometric IDs to be stolen.
Options for changing the biometric ID immediately in case it is already stolen.