A New Approach for Real-time Iris Liveness Detection Based on Eye Movement’s Features

Abstract

This research introduces a new approach for iris liveness detection based on eye movement’s features. Given 50 valid eye frames from real-time video stream, this research identify them as the images that were taken whether directly from humans’ eyes or not. Our method ensures the selected images for further processing are eye ones without user’s cooperation during the sampling process. In addition, it also controls the application timeout by identifying either fake iris or low quality eye images. In my study, this Research used two features to model eye movements: (1) the width/height ratio of the eye outline and (2) the distance from the pupil center to the eye centroid. In order to model the acquired data distribution, this research used a bag of two-component Gaussian Mixing Models (GMMs). The class label of the acquired data depends on the GMMs which it belongs. The average accuracy of my proposed method on my and IriTech, Inc.’s databases was 94.704% and 100%, respectively. This result indicated that my method can make real-time iris-based systems work automatically with high security.