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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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Veins of Upper Limbs01:17

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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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Related Experiment Video

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

Hyung Gil Hong1, Min Beom Lee2, Kang Ryoung Park3

  • 1Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea. hell@dongguk.edu.

Sensors (Basel, Switzerland)
|June 8, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel convolutional neural network (CNN) method for robust finger-vein recognition, outperforming traditional techniques. The CNN approach enhances accuracy across diverse databases and environmental conditions.

Keywords:
CNNbiometricsfinger-vein recognitiontexture feature extraction

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Conventional finger-vein recognition relies on vein line extraction or texture analysis.
  • Inaccurate vein line detection and the need for experimental filter selection limit current methods' accuracy and adaptability.

Purpose of the Study:

  • To propose a robust finger-vein recognition method using convolutional neural networks (CNNs).
  • To overcome limitations of traditional methods regarding database variations and environmental changes.

Main Methods:

  • Developed a novel finger-vein recognition approach based on convolutional neural networks (CNNs).
  • Evaluated the method using two newly constructed finger-vein databases and the open-source SDUMLA-HMT database.

Main Results:

  • The proposed CNN-based method demonstrated superior performance compared to conventional techniques.
  • Achieved enhanced recognition accuracy across different finger-vein databases and varying environmental conditions.

Conclusions:

  • The CNN-based finger-vein recognition method offers improved robustness and accuracy.
  • This approach effectively addresses the limitations of traditional finger-vein recognition systems.