Pupil Size Measurement and Application to Iris Presentation Attack Detection

Abstract

AbstractThis chapter presents a comprehensive study on the application of stimulated pupillary light reflex to presentation attack detection (PAD) that can be used in iris recognition systems. A pupil, when stimulated by visible light in a predefined manner, may offer sophisticated dynamic liveness features that cannot be acquired from dead eyes or other static objects such as printed contact lenses, paper printouts, or prosthetic eyes. Modeling of pupil dynamics requires a few seconds of observation under varying light conditions that can be supplied by a visible light source in addition to the existing near infrared illuminants used in iris image acquisition. The central element of the presented approach is an accurate modeling and classification of pupil dynamics that makes mimicking an actual eye reaction difficult. This chapter discusses new data-driven models of pupil dynamics based on recurrent neural networks and compares their PAD performance to solutions based on the parametric Clynes-Kohn model and various classification techniques. Experiments with 166 distinct eyes of 84 subjects show that the best data-driven solution, one based on long short-term memory, was able to correctly recognize 99.97% of attack presentations and 98.62% of normal pupil reactions. In the approach using the Clynes-Kohn parametric model of pupil dynamics, we were able to perfectly recognize abnormalities and correctly recognize 99.97% of normal pupil reactions on the same dataset with the same evaluation protocol as the data-driven approach. This means that the data-driven solutions favorably compare to the parametric approaches, which require model identification in exchange for a slightly better performance. We also show that observation times may be as short as 3 s when using the parametric model, and as short as 2 s when applying the recurrent neural network without substantial loss in accuracy. Compared to the last edition, this chapter offers also an open-source/open-hardware Raspberry Pi Zero-based device, with Python implementation, for wireless pupil size measurement. It’s a client-server system, in which Pi is responsible for appropriate camera stream acquisition and transferring data wirelessly to a host computer, which then performs data processing and pupil size estimation using algorithms based on convolutional neural networks. The proposed system can be used in various research efforts related to pupil size measurement, including PAD, where acquisition needs to be done without constraints related to the subject’s position. Along with this chapter we offer: (a) all-time series representing pupil dynamics for 166 distinct eyes used in this study, (b) weights of the trained recurrent neural network offering the best performance, (c) source codes of the reference PAD implementation based on Clynes-Kohn parametric model, (d) all PAD scores that allows the reproduction of the plots presented in this chapter, and (e) all codes necessary to build a Raspberry Pi-based pupillometer.