An Investigation of the Effectiveness of Template Protection Methods on Protecting Privacy During Iris Spoof Detection

With the development of iris biometrics, more and more industries and fields begin to apply iris recognition methods. However, as technology advances, attackers try to use printed iris images or artifacts and so on to spoof iris recognition systems. As a result, iris spoof detection is becoming an increasingly important area of research. The employment of spoof detection enhances the security and reliability of iris recognition systems, but an attacker can still subvert the systems by stealing iris data during the spoof detection phase. In this paper, we design a framework called TPISD to solve the issue. TPISD mainly employs template protection methods to protect iris data during the spoof detection phase as well as client to server phase. Specifically, iris data are converted into cancelable and irreversible templates after data capture. These templates are then used to train the spoof detection model. Eventually, during the spoof detection phase, protected templates are used as input, rather than the original iris images. Experiments conducted on CASIA-Syn and CASIA-Interval datasets demonstrate that the application of iris template protection techniques to the spoof detection model may result in a reduction on recognition accuracy, but it can enhance the security of the spoof detection model. This work verifies the feasibility of employing iris template protection methods to protect iris data during the spoof detection.