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LFLDNet: Lightweight Fingerprint Liveness Detection Based on ResNet and Transformer.

Kang Zhang1, Shu Huang1, Eryun Liu2

  • 1Engineering Research Centre of Molecular & Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an 710071, China.

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Summary
This summary is machine-generated.

A new lightweight network enhances fingerprint liveness detection against spoofing. It uses CycleGAN for generalization and a ResNet with self-attention for improved performance and reduced complexity, achieving high accuracy on benchmark datasets.

Keywords:
fingerprint liveness detectionlightweightmulti-head self-attentionspoofing attackstransformer

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Area of Science:

  • Biometrics and Security
  • Artificial Intelligence
  • Computer Vision

Background:

  • Fingerprint recognition systems are advancing rapidly, making liveness detection crucial for preventing spoofing attacks.
  • Convolutional neural networks show promise for liveness detection but require improvements in generalization to unknown materials and computational efficiency.
  • Existing methods struggle with the generalization ability for unknown materials and the computational complexity of deep learning models.

Purpose of the Study:

  • To propose a novel, lightweight network for robust fingerprint liveness detection.
  • To enhance the generalization capability of models against spoofing attempts using diverse materials.
  • To improve detection performance while reducing computational overhead in fingerprint identification systems.

Main Methods:

  • A novel lightweight network combining foreground extraction, fingerprint image blocking, CycleGAN-based style transfer, and an improved ResNet with a multi-head self-attention (MHSA) mechanism.
  • CycleGAN was employed to improve the model's generalization ability for fake fingerprints derived from unknown materials.
  • An improved ResNet incorporating a Transformer with MHSA was utilized to enhance detection performance and decrease computational load.

Main Results:

  • The proposed method demonstrated effective Region of Interest (ROI) extraction and an end-to-end data structure, increasing data volume.
  • Experiments on LivDet2011, LivDet2013, and LivDet2015 datasets showed competitive results, with an average classification error of 1.72% on LivDet2015 across all sensors.
  • The network achieved 95.27% accuracy on small-area fingerprints with a significantly reduced parameter count (0.83 M).

Conclusions:

  • The developed lightweight network effectively distinguishes real from fake fingerprints, addressing limitations of previous deep learning approaches.
  • The integration of CycleGAN and MHSA-based ResNet significantly enhances model generalization and detection accuracy while minimizing computational resources.
  • The proposed method offers a promising solution for secure and efficient fingerprint liveness detection, particularly for challenging scenarios involving unknown spoofing materials.