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Related Concept Videos

Veins of Upper Limbs01:17

Veins of Upper Limbs

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The human circulatory system, a marvel of biological engineering, is a complex network of vessels that transport blood throughout the body. Among these, the veins responsible for carrying blood from the upper limbs are divided into two categories: deep and superficial.
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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: Mar 15, 2026

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An Efficient Finger Vein Recognition Method Based on Improved Lightweight MobileNet.

Xuhui Zhang1, Yuxi Liu1, Yixin Yan1

  • 1School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China.

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|March 14, 2026
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Summary

This study introduces an efficient finger vein recognition system using a lightweight convolutional neural network (LCNN). The method achieves high accuracy and fast processing, making it suitable for real-time biometric security.

Keywords:
deep learningefficient identificationfinger vein recognitionimage preprocessinglightweight neural networks

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision
  • Machine Learning

Background:

  • Finger vein recognition is a robust biometric technology for identity authentication.
  • Conventional methods face challenges in feature representation and efficiency under varying conditions.
  • Lightweight and efficient biometric systems are needed for real-time applications.

Purpose of the Study:

  • To develop an efficient finger vein recognition approach using a lightweight convolutional neural network (LCNN).
  • To improve feature representation and computational efficiency for biometric authentication.
  • To enable real-time and embedded deployment of finger vein recognition systems.

Main Methods:

  • Implemented a lightweight convolutional neural network (LCNN) architecture.
  • Integrated a multi-stage image preprocessing pipeline: vein detection, denoising, and texture enhancement.
  • Employed compact feature modeling within the LCNN framework.

Main Results:

  • Achieved high recognition accuracies of 97.1% on the SDUMLA-HMT dataset and 98.3% on the Lab-Vein dataset.
  • Demonstrated significant reductions in parameter complexity and computational cost.
  • Attained an average inference time of 12.6 ms, confirming strong real-time capability.

Conclusions:

  • The proposed LCNN-based finger vein recognition method offers superior accuracy and efficiency.
  • The system is suitable for real-time applications and embedded deployment.
  • This approach advances lightweight biometric recognition systems with a balance of accuracy and speed.