Improving Iris Localization Performance Using Image Processing Tools: Multi-Input Databases

The interface of computer technologies and biology is having a huge impact on society. Human recognition research projects promises new life to many security-consulting. Iris recognition is considered to be the most reliable biometric authentication system. Image quality plays a crucial role in any pattern matching system. Three different iris databases have been employed for comparison of performance of proposed iris detection and isolation technique based on morphological features. CASIA, UPOL, and UBIRIS databases were processed as different types of noise like iris obstruction by eyelids, eyelashes, lighting reflections, and poor focused images. To process the iris patterns in an efficient and effective way against existing methods, many simple and effective image processing methods have been presented in image selection, iris preprocessing, iris segmentation, iris localization, and isolation. Experimental results show that our method achieves an accuracy of 100% for select best iris data, and 99% for isolate iris region.