Dual iris based human identification

In this paper, a dual iris based human identification system that increases the accuracy and the performance of a typical human iris recognition system is proposed. The system detects and then isolates and extracts the iris region from eye image. It then sets the radial and angular resolution for the extracted iris region and maps the circular region into rectangular polar coordinates 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 method measures this Hamming Distance using iris images from a single eye. However, the proposed method takes images from both eyes simultaneously for comparison process which shows an increased accuracy and performance. Using the proposed method CASIA Iris database V3, false positive rate and false negative rate were found to be 0% and 9.96% respectively while the overall accuracy was 99.92%.