Fingerprint Presentation Attack Detector Using Global-Local Model

Haozhe Liu, Wentian Zhang, Feng Liu, Haoqian Wu, Linlin Shen

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This article thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module; 2) the local module; and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ~10% in terms of TDR (91.19% versus 80.74%).

Original languageEnglish
JournalIEEE Transactions on Cybernetics
DOIs
Publication statusAccepted/In press - 2021
Externally publishedYes

Keywords

  • Computer vision
  • Detectors
  • Ensemble learning
  • Fingerprint recognition
  • Image sensors
  • presentation attack (PA) detection
  • rethinking strategy
  • self-supervised learning
  • Semantics
  • Sensors
  • Visualization
  • weakly supervised learning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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