Gaze estimation as a framework for iris liveness detection

This work investigates the possibility of detecting iris print-attacks via the analysis of a number of gaze-related features acquired in a process of eye tracking. Gaze estimation algorithms employ models based on the physical structure and function of the eye, providing thus a number of salient features that can be potentially employed for the detection of spoofing print-attacks. In our study, a combined dataset was assembled for the investigation of these features, consisting of eye movement recordings and the corresponding iris images collected from 100 subjects. The collected iris images were utilized in direct implementation of iris print-attacks against an eye tracking device. We developed a methodology for the detection of spoof indicative artifacts in the recorded signals, and fed the extracted features from the live and spoof eye signals into a two-class SVM classifier. The obtained results indicate a best correct classification rate (CCR) of 95.7%. Furthermore, we demonstrate the moderate decrease in liveness detection rates during subsampling of the eye movement signal to frequencies as low as 15 Hz. This result indicates the usefulness of running gaze estimation algorithms on existing iris recognition devices where such sampling frequency rate is common.