Face Liveness Detection Benchmark based on Stereo Matching

Abstract

In this paper, a face liveness detection benchmark is established and maintained, wherein 400 images pairs captured with binocular camera are made openly available for research purposes. This image dataset contains numbers of people with varied expressions, illumination, and background environment conditions, etc., among which 200 image pairs characterize lively human faces, and the other half are planar face pictures. The benchmark provides a platform for researchers to test stereo matching algorithms for liveness detection, where the detection performance is evaluated via a binary classification on the detection response for being a lively human or not. The feasibility of SIFT features are verified based on a comparative analysis of the classification result, and a set of optimal parameters for the classification is given which provides a reference for further research. * denotes the equal contributions.