IRIS-Based Human Identity Recognition with Deep Learning Methods

One of the most important computer system modules is the one in charge of user security. It has been demonstrated that simple logins and passwords cannot provide high efficiency and are simple for hackers to crack. The popular substitute is identity identification using bio-metrics. Iris as a biometric characteristic has attracted increased attention in recent years. It was brought on by the high efficiency and precision this quantifiable aspect ensured. Throughout the literature, the effects of this curiosity can be seen. Several authors have put forth a variety of various approaches. This paper describe methods for an irisbased algorithm for recognizing human identity. Artificial neural networks (ANN) and a CNN-based transfer learning model (Mobile-net) were employed in the classification process. Once the output has been categorized, the iris component is segmented using a process called segmentation. Tests that have been run have demonstrated that the suggested procedure can produce results that are adequate.