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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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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Related Experiment Video

Updated: Nov 19, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Transformers and Generative Adversarial Networks for Liveness Detection in Multitarget Fingerprint Sensors.

Soha B Sandouka1, Yakoub Bazi1, Naif Alajlan1

  • 1Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Sensors (Basel, Switzerland)
|January 27, 2021
PubMed
Summary

This study enhances fingerprint presentation attack detection (PAD) using transformers and generative adversarial networks (GANs). The novel approach significantly improves accuracy in cross-sensor and cross-material scenarios.

Keywords:
fingerprintgenerative adversarial networkliveness detectiontransformer

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Area of Science:

  • Biometrics and Security
  • Artificial Intelligence
  • Computer Vision

Background:

  • Fingerprint systems are widely used but vulnerable to presentation attacks.
  • Effective detection across different sensors and materials is crucial for security.
  • Existing methods struggle with distribution shifts in fingerprint data.

Purpose of the Study:

  • To develop an improved fingerprint presentation attack detection (PAD) method.
  • To enhance the generalization ability of PAD systems in cross-sensor and cross-material settings.
  • To reduce distribution shifts between fingerprint representations from multiple sensors.

Main Methods:

  • Proposed a novel architecture combining transformers and generative adversarial networks (GANs).
  • Focused on reducing distribution shifts in fingerprint data representations.
  • Validated the methodology on the public LivDet2015 dataset.

Main Results:

  • Achieved a significant increase in average classification accuracy.
  • Accuracy improved from 68.52% to 83.12% after adaptation.
  • Demonstrated improved generalization in cross-sensor and cross-material evaluations.

Conclusions:

  • The proposed transformer and GAN-based approach effectively enhances fingerprint PAD.
  • The method shows strong performance in diverse and challenging cross-sensor/material environments.
  • This work contributes to more robust and reliable biometric security systems.