Raju, Mehedi HasanLohr, Dillon J.Komogortsev, Oleg V.2024-10-142024-10-142022-06Mehedi Hasan Raju, Dillon J Lohr, and Oleg Komogortsev. 2022. Iris Print Attack Detection using Eye Movement Signals. In 2022 Symposium on Eye Tracking Research and Applications (ETRA '22). Association for Computing Machinery, New York, NY, USA, Article 70, 1–6. https://doi.org/10.1145/3517031.3532521https://hdl.handle.net/10877/19663Iris-based biometric authentication is a wide-spread biometric modality due to its accuracy, among other benefits. Improving the resistance of iris biometrics to spoofing attacks is an important research topic. Eye tracking and iris recognition devices have similar hardware that consists of a source of infrared light and an image sensor. This similarity potentially enables eye-tracking algorithms to run on iris-driven biometrics systems. The present work advances the state-of-the-art of detecting iris print attacks, wherein an imposter presents a printout of an authentic user’s iris to a biometrics system. The detection of iris print attacks is accomplished via analysis of the captured eye movement signal with a deep learning model. Results indicate better performance of the selected approach than the previous state-of-the-art.Dataset58.2 MB1 file (.zip)eniris-based biometriceye trackingeye movement signalsDatasethttps://doi.org/10.1145/3517031.3532521