Machine learning-based iris liveness identification using fragmental energy of cosine transformed iris images

Abstract

Iris biometric identification provides a contactless authentication preventing the spread of COVID-19 like diseases. These systems are made vulnerable and unsafe because of the spoofing attacks attempted with the help of contact lenses, video replays and print attacks. The paper proposes the iris liveness detection method to mitigate spoofing attacks, taking fragmental coefficients of cosine transformed iris image to be used as features. Seven variants of feature formation are considered in experimental validations of the proposed method, and the features are used to train eight assorted machine learning classifiers and ensembles for iris liveness identification. Recall, F-measure, precision and accuracy are used to evaluate performances of the projected iris liveness identification variants. The experimentation carried out on four standard datasets have shown better iris liveness identification by the fragmental coefficients of cosine transformed iris image with size 4 * 4 using random forest algorithm having 99.18% accuracy immediately followed by an ensemble of classifiers.