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Related Concept Videos

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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 C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...

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Related Experiment Video

Updated: May 21, 2026

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

Published on: September 2, 2020

Improving fingerprint verification using minutiae triplets.

Miguel Angel Medina-Pérez1, Milton García-Borroto, Andres Eduardo Gutierrez-Rodríguez

  • 1Centro de Bioplantas, Universidad de Ciego de Ávila, Ciego de Ávila, Cuba. migue@bioplantas.cu

Sensors (Basel, Switzerland)
|June 28, 2012
PubMed
Summary
This summary is machine-generated.

A new fingerprint matching algorithm, M3gl, overcomes limitations of minutia triplet methods. It achieves superior accuracy and faster matching times for fingerprint recognition.

Keywords:
fingerprint verificationminutiae descriptorminutiae triplet

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Related Experiment Videos

Last Updated: May 21, 2026

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Application of DNA Fingerprinting using the D1S80 Locus in Lab Classes

Published on: July 17, 2021

Area of Science:

  • Biometrics
  • Computer Science
  • Pattern Recognition

Background:

  • Minutia triplet algorithms are crucial for fingerprint recognition but suffer from accuracy issues.
  • Existing methods are sensitive to minutiae order, reflection, and relative directions.

Purpose of the Study:

  • Introduce M3gl, a novel fingerprint matching algorithm.
  • Address the limitations of current minutia triplet-based approaches.

Main Methods:

  • Developed a new feature representation with clockwise-arranged minutiae.
  • Implemented a similarity measure with triplet shifting for optimal correspondence.
  • Incorporated a global matching procedure to maximize minutiae alignment.
  • Added optimizations for faster non-matching triplet discarding.

Main Results:

  • M3gl demonstrated higher accuracy compared to six other verification algorithms.
  • The algorithm achieved the lowest matching time among tested methods.
  • Performance was validated on FVC2002 and FVC2004 fingerprint databases.

Conclusions:

  • M3gl offers an improved solution for fingerprint matching.
  • The novel approach enhances both accuracy and efficiency in fingerprint recognition systems.