Face presentation attack detection in mobile scenarios: A comprehensive evaluation

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摘要

The vulnerability of face recognition systems to different presentation attacks has aroused increasing concern in the biometric community. Face presentation detection (PAD) techniques, which aim to distinguish real face samples from spoof artifacts, are the efficient countermeasure. In recent years, various methods have been proposed to address 2D type face presentation attacks, including photo print attack and video replay attack. However, it is difficult to tell which methods perform better for these attacks, especially in practical mobile authentication scenarios, since there is no systematic evaluation or benchmark of the state-of-the-art methods on a common ground (i.e., using the same databases and protocols). Therefore, this paper presents a comprehensive evaluation of several representative face PAD methods (30 in total) on three public mobile spoofing datasets to quantitatively compare the detection performance. Furthermore, the generalization ability of existing methods is tested under cross-database testing scenarios to show the possible database bias. We also summarize meaningful observations and give some insights that will help promote both academic research and practical applications.

论文关键词:Face presentation attack,Face recognition,Performance evaluation,Biometrics

论文评审过程:Received 12 October 2019, Accepted 5 November 2019, Available online 12 November 2019, Version of Record 21 January 2020.

论文官网地址:https://doi.org/10.1016/j.imavis.2019.11.004