Iris Recognition Using Artificial Neural Network

Iris recognition has emerged as one of the most widely used and effective techniques for personal verification. It leverages the unique features of the human eye, specifically the distinct patterns found in the iris or, more precisely, the pupil. Biometric systems based on iris scanning analyze these unique iris patterns to confirm and authenticate an individual's identity. They are widely used as human eye has unique features particularly the iris. Human iris have random variations and complex texture which are stable with time even visible between identical twins. It’s replication and forgery are almost impossible. For training and testing our model we used CASIA-Iris-Interval database as camera used to collect this data uses a circular NIR LED array [1].Near Infrared LED has a suitable luminous flux for imaging human iris for recognition. Our results demonstrate that the ANN model outperforms traditional machine learning algorithms, such as Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), in terms of both accuracy and robustness [5]. The model showed high accuracy in identifying iris patterns, even under varying conditions like changes in lighting, pupil dilation, and partial occlusions. This highlights the potential of deep learning techniques to overcome the challenges faced by conventional biometric systems, which often struggle with high-dimensional data and manual feature extraction [18].