Iris Liveness Detection Based on Light Field Imaging

Abstract

Light-field (LF) imaging is a new method to capture both intensity and direction information of visual objects, providing promising solutions to biometrics. Iris recognition is a reliable personal identification method, however it is also vulnerable to spoofing attacks, such as iris patterns printed on contact lens or paper. Therefore iris liveness detection is an important module in iris recognition systems. In this paper, an iris liveness detection approach is proposed to take full advantages of intrinsic characteristics in light-field iris imaging. LF iris images are captured by using lab-made LF cameras, based on which the geometric features as well as the texture features are extracted using the LF digital refocusing technology. These features are combined for genuine and fake iris image classification. Experiments were carried out based on the self-collected near-infrared LF iris database, and the average classification error rate (ACER) of the proposed method is 3.69%, which is 5.94% lower than the best state-of-the-art method. Experimental results indicate the proposed method is able to work effectively and accurately to prevent spoofing attacks such as printed and screen-displayed iris input attacks.