Implementing a Robust Iris Recognition System using Feedforward Neural Network Techniques

Iris recognition is a technique that analyses an individual's iris image to use an artificial neural network (ANN) based feedforward neural network (FNN). The FNN contains several layers of neurons that help it analyze input data and extract important information. Techniques including image preparation, feature extraction, and classification are used to increase accuracy, With accuracy rates of over 99.98%, FNN-based iris recognition has produced impressive results. Receiver Operating Characteristic curve, a true-positive rate, False Positive Rate, as well as other performance indicators are used to measure the system's accuracy. However, challenges such as motion blur and noisy images might have a significant impact on the way the FNN-based iris identification system performs Such challenges are now being addressed in existing research in an effort to increase the efficiency and dependability of FNN-based iris recognition systems for a wide range of applications, such as immigration control, remote access, and banking transactions.