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A Survey on Deep Learning Techniques for Fingerprint Presentation Attack Detection.

Hailin Li1, Raghavendra Ramachandra1

  • 1SAFE Center, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

Deep learning methods significantly enhance fingerprint presentation attack detection (FPAD) for secure authentication. This review categorizes recent deep learning FPAD techniques, datasets, and metrics, highlighting future research directions.

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Area of Science:

  • Computer Science
  • Biometrics
  • Artificial Intelligence

Background:

  • Fingerprint authentication systems face security vulnerabilities, necessitating robust anti-spoofing measures.
  • Traditional handcrafted feature-based methods for fingerprint presentation attack detection (FPAD) lack generalization.
  • Deep learning approaches have emerged as the dominant and high-performing paradigm for FPAD in recent years.

Purpose of the Study:

  • To provide a comprehensive overview of deep learning-based fingerprint presentation attack detection (FPAD) methods.
  • To categorize and analyze recent advancements in deep learning FPAD.
  • To discuss benchmark metrics and available datasets for evaluating FPAD techniques.

Main Methods:

  • Literature review focusing exclusively on deep learning-based FPAD techniques.
Keywords:
biometricsdeep learningfingerprint presentation attack detection

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  • Categorization of methods based on network architecture, feature extraction, or learning strategy.
  • Analysis of performance metrics and publicly available datasets used in the field.

    • Main Results:

    • Deep learning methods demonstrate superior performance and generalization capabilities compared to traditional approaches.
    • Various deep learning architectures and strategies have been successfully applied to FPAD.
    • Standardized benchmark metrics and datasets are crucial for reproducible research and comparison.

    Conclusions:

    • Deep learning-based FPAD is critical for secure biometric authentication.
    • Further research is needed to address remaining challenges and explore novel deep learning architectures.
    • Standardized evaluation protocols and datasets will accelerate progress in the field.