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Related Concept Videos

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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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
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Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection.

Ngoc Tuyen Le1, Duc Huy Le2, Jing-Wein Wang1

  • 1Institute of Photonic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive method for detecting singular points (SPs) in fingerprint images. The reliable detection of core and delta points enhances fingerprint identification and classification systems.

Keywords:
blurring detectionboundary segmentationfingerprint image enhancementfingerprint qualitysingular point detection

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Area of Science:

  • Biometrics
  • Image Processing
  • Pattern Recognition

Background:

  • Automated fingerprint identification systems rely on singular points (SPs) like core and delta points.
  • SPs are crucial for fingerprint registration, orientation field estimation, and classification.
  • Existing methods may face challenges in reliably detecting SPs.

Purpose of the Study:

  • To propose an adaptive and reliable method for detecting singular points (SPs) in fingerprint images.
  • To improve the accuracy of fingerprint registration, orientation field estimation, and classification.
  • To develop an algorithm that effectively handles background noise and image blurring.

Main Methods:

  • An innovative image enhancement technique using singular value decomposition (SVD) for background removal.
  • A blurring detection and boundary segmentation algorithm to identify the region of interest.
  • An adaptive method combining wavelet extrema and the Henry system for core point detection.

Main Results:

  • The proposed method successfully detects singular points (SPs) in fingerprint images.
  • Experimental results on FVC2002 DB1 and DB2 databases demonstrate reliable SP detection.
  • The method effectively removes background noise and segments the fingerprint region.

Conclusions:

  • The developed adaptive method reliably detects singular points (SPs) in fingerprint images.
  • This technique offers a robust solution for enhancing fingerprint analysis and identification.
  • The proposed approach shows significant potential for improving automated fingerprint systems.