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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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Mask-guided network for finger vein feature extraction and biometric identification.

Haohan Bai1,2, Yubo Tan2, Yong-Jie Li1,2

  • 1Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China (UESTC), Huzhou 313001, China.

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Summary

This study introduces a novel finger vein recognition algorithm that enhances feature extraction by using segmentation-assisted classification and a hybrid loss function. The method significantly improves accuracy in identifying finger vein patterns, even with complex backgrounds.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Challenges in finger vein recognition include complex backgrounds, low image quality, and poor feature discriminability.
  • Existing methods struggle to effectively isolate and learn from relevant vein features.

Purpose of the Study:

  • To propose a robust feature extraction algorithm for finger vein recognition.
  • To improve the accuracy and discriminative power of extracted finger vein features.
  • To address the limitations of complex backgrounds and low-quality images in biometric systems.

Main Methods:

  • A segmentation-assisted classification approach uses a rough finger vein mask to guide feature learning.
  • A feature pyramid module fuses multi-scale features, followed by a spatial attention module to generate a spatial weight map.
  • A refined mask, derived from classical vein skeleton extraction, constrains the spatial weight map learning.
  • A hybrid loss function combining triplet loss and cross-entropy loss enhances feature vector discriminability.

Main Results:

  • The proposed algorithm achieved low equal error rates (EER) of 2.50% on SDUMLA, 0.20% on MMCBNU, and 0.14% on FVUSM.
  • Demonstrated superior performance in finger vein recognition across multiple public datasets.
  • Effective handling of complex backgrounds and improved feature learning for vein patterns.

Conclusions:

  • The developed feature extraction algorithm significantly enhances finger vein recognition accuracy.
  • Segmentation-assisted classification and hybrid loss functions are effective in improving feature discriminability.
  • The method offers a promising solution for reliable and accurate biometric identification using finger veins.