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Related Experiment Video

Updated: May 5, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition.

Qiong Yao1, Dan Song1, Xiang Xu1

  • 1Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary
This summary is machine-generated.

A novel visual feature-guided diamond convolutional network (VF-DCN) enhances finger vein recognition (FVR) by addressing data scarcity and image quality issues. This method achieves high accuracy and robustness with fewer parameters, improving biometric security.

Keywords:
Log-Gabordiamond convolutional networkfinger veinvisual feature-guided

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Finger vein (FV) biometrics offer high security and non-contact identity authentication.
  • Existing finger vein recognition (FVR) systems face challenges with limited training data and inconsistent image quality.

Purpose of the Study:

  • To introduce a novel convolutional neural network, the visual feature-guided diamond convolutional network (VF-DCN), to improve FVR system performance.
  • To address data scarcity and image quality issues in FVR through an innovative network architecture and unsupervised training.

Main Methods:

  • Developed VF-DCN, a multi-scale, multi-orientation convolutional neural network utilizing Log-Gabor filters for kernel tuning.
  • Implemented a diamond-shaped convolutional kernel architecture inspired by human vision, optimizing filter allocation across scales.
  • Employed a three-layer configuration and fully unsupervised training for simplicity and performance.

Main Results:

  • VF-DCN achieved exceptional Equal Error Rates (EERs) as low as 0.17% and Accuracy Rates (ACC) up to 100% across four diverse FV databases.
  • Demonstrated superior recognition accuracy and robustness compared to existing FVR approaches.
  • Exhibited a reduced number of parameters and lower model complexity.

Conclusions:

  • The VF-DCN effectively overcomes limitations in FVR systems, offering high accuracy and robustness.
  • The proposed network architecture and training strategy provide a promising solution for secure and efficient biometric authentication.
  • VF-DCN presents a computationally efficient and highly accurate method for finger vein recognition.