DEVELOPMENT OF ENHANCED IRIS AUTHENTICATION BASED BIOMETRIC SYSTEM

Abstract

The use of biometric authentication, which uses a person's fingerprint, face, iris, handwriting, or other distinctive physical or behavioural characteristics to identify them, is becoming more and more common. When using traditional authentication methods, password protection and memory loss become challenges. This is where biometric authentication steps in to help. The Iris provides the highest degree of uniqueness, universality, precision, and reliability of all the biometrics now in use. The proposed system aims to enhance the security and accuracy of biometric identification through the integration of advanced image processing techniques. The methodology consists of multiple steps: pre-processing (histogram equalization), segmentation (Canny edge and Hough transform), normalizing (Daugman's rubber sheet model), feature extraction (Gabor filter), and matching (Hamming Distance). While segmentation makes it easier to isolate pertinent iris information, histogram equalization attempts to improve image contrast. Normalization guarantees that features are represented consistently, and the process of feature extraction that follows, extracts discriminative data that is essential for precise authentication. In order to compare retrieved features and assess how similar Genuine and Imposter iris patterns are, the matching stage uses a strong algorithm. The average performance metrics obtained reveal promising results, with Recall, Specificity, FAR (False Acceptance Rate), FRR (False Rejection Rate), Precision, F-measure, and Accuracy are reported as 85.20%, 58.87%, 38.11%, 21.97%, 63.33%, 64.80%, and 99.57%, respectively. These results highlight how well the suggested method works to achieve high accuracy and reliability levels, with a focus on how well it can reduce the rates of false acceptance and rejection...