Project Title: Iris Template Attack Detection using ML and DL Methods

Team Members

Dr. G. Padmavathi, Dean - PSCS, Professor, Department of Computer Science

Dr. D. Shanmugapriya, Assistant Professor and Head, Department of Information Technology

Ms. A. Roshni, Research Assistant, Centre for Cyber Intelligence, DST - CURIE - AI

Ms. A. Aysha, M.Sc Information Technology

Project Summary

The iris is a guarded, outwardly visible function that maintains its genomic structure during the entire adulthood. It's a worthy choice to be used as a biometric for identifying persons due to these characteristics. Each people's iris is distinct. Nevertheless Even fraternal identical twins and a person's left and right irises have distinct features. The chances of discovering two persons with the same iris patterns are estimated to be one in 1052. As biometric identification systems become more common, an attacker's incentive to stage a system compromise grows, as does the requirement to assure system security and integrity. 

The Objective is to find the iris template attack in iris template of each user that is being stored on the background. A mix of multiple pre-processing and classification algorithms are being involved and used to this suggested project such as Eye Detection, Iris Detection, Morphological Operations, Edge Detection using Contour and Iris Segmentation. The template further undergoes possible Template Attack to create the attacked template image. The model is being built using deep learning technique namely Convolutional Neural Network (CNN) without max pooling which provides 97.50% Accuracy and CNN with max pooling gives 100% Accuracy. In Machine Learning (ML) techniques, logistic regression is applied to classify and detect the attacked template from genuine iris template and it gives 90% accuracy.