HsIrisNet: Histogram Based Iris Recognition to Allay Replay and Template Attack Using Deep Learning Perspective

Abstract

Iris biometric is a widely deployed tool for biometric based user authentication. Its success has paved path for several security related attacks on this biometric. In this paper, we propose a deep learning perspective to solve two of these attacks—replay attack and database attack simultaneously. The proposed architecture HsIrisNet, a convolutional neural network (CNN), is trained and tested on two publicly available databases Casia-Iris-Interval v4 DB and IIT Delhi DB. The proposed approach HsIrisNet uses histograms of the selective robust regions followed by image upsampling to authenticate the user. These robust regions have been found to be sufficient enough to authenticate the user, enabling non-deterministic approach. The use of these selective regions for authentication enables the system to mitigate replay attack. While, the CNN model does not require saving any biometric data to the database, which mitigates database attack from the system. The comparison of proposed approach with existing state-of-art techniques show better performance of the proposed approach.