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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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Updated: Nov 10, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features.

Anas Husseis1, Judith Liu-Jimenez1, Raul Sanchez-Reillo1

  • 1University Group for ID Technologies (GUTI), University Carlos III of Madrid (UC3M), Av. de la Universidad 30, 28911 Madrid, Spain.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

This study introduces a new method for detecting fake fingerprints using spatio-temporal features and SVM classification. The system effectively distinguishes real fingerprints from presentation attacks, achieving low error rates on optical and thermal sensors.

Keywords:
anti-spoofingfingerprintpresentation attackpresentation attack detection

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Area of Science:

  • Biometrics and Security
  • Pattern Recognition
  • Computer Vision

Background:

  • Presentation attacks pose a significant threat to fingerprint recognition systems.
  • Existing methods struggle to effectively detect diverse presentation attack species.
  • Dynamic analysis of fingerprint patterns is crucial for robust security.

Purpose of the Study:

  • To develop and evaluate a novel mechanism for dynamic presentation attack detection in fingerprint systems.
  • To enhance the security of fingerprint recognition against various spoofing techniques.
  • To validate the proposed method using standardized experimental protocols.

Main Methods:

  • Utilized five spatio-temporal feature extractors to capture dynamic fingerprint pattern variations.
  • Integrated ridge/valley patterns with temporal dynamics from fingerprint videos.
  • Employed a Support Vector Machine (SVM) classifier with a second-degree polynomial kernel for classification.
  • Conducted experiments and evaluations adhering to the ISO/IEC 30107-3:2017 standard.

Main Results:

  • The proposed approach demonstrated high efficiency in detecting presentation attacks.
  • Achieved a low BPCER (Best Possible Classification Error Rate) of 1.11% for optical sensors.
  • Achieved a BPCER of 3.89% for thermal sensors, both at a 5% APCER (Actual Presentation Attack Detection Error Rate).

Conclusions:

  • The novel spatio-temporal feature extraction and SVM classification effectively detect fingerprint presentation attacks.
  • The method offers robust performance across different sensor types (optical and thermal).
  • The system meets high security standards, significantly reducing the likelihood of successful spoofing attempts.