Light Field-Based Face Presentation Attack Detection: Reviewing, Benchmarking and One Step Further

Abstract

Vulnerability of face recognition systems to presentation attacks has attracted increasing attention from the biometrics and forensics communities. Moreover, the recent availability of light field cameras is opening new possibilities for designing improved face presentation attack detection solutions. In this context, this paper provides the first review and benchmarking study in the literature on light field-based face presentation attack detection solutions. State-of-the-art solutions are assessed in terms of accuracy, generalization and complexity, using a common, representative evaluation framework. This paper also proposes a novel face presentation attack detection solution, based on a histogram of oriented gradients descriptor, which exploits the disparity information available in light field imaging. The evaluation of the proposed face presentation attack detection solution for different presentation attack types shows a very effective and stable performance, notably performing better than the state-of-the-art alternatives.