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Related Experiment Video

Updated: Jun 25, 2025

A New Best Practice for Validating Tail Vein Injections in Rat with Near-infrared-Labeled Agents

A New Best Practice for Validating Tail Vein Injections in Rat with Near-infrared-Labeled Agents

Published on: April 19, 2019

Study for lightweight finger vein recognition based on a small sample.

Yi Ding^1,2,3, Kai Wang⁴, Xiaojun Wu⁵

¹School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China. dingyi@zust.edu.cn.

Scientific Reports

|May 25, 2024

Summary

This summary is machine-generated.

This study introduces a lightweight finger vein recognition algorithm using a modified VGG-19 network and Gabor filters. The novel approach achieves high accuracy (98.45%) and improved robustness for biometric identification.

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Area of Science:

Biometrics
Computer Vision
Machine Learning

Background:

Finger vein recognition faces challenges like low-temperature effects and generalization.
Existing algorithms may require extensive training data and computational resources.

Purpose of the Study:

To develop a lightweight and efficient finger vein recognition algorithm.
To enhance the algorithm's robustness and accuracy using a small sample size.

Main Methods:

A Gabor filter preprocesses images to simulate low-temperature conditions, improving model generalization.
A modified VGG-19 network with reduced layers and adjusted activation functions creates a lightweight architecture.
A multi-attention mechanism is integrated to enhance feature extraction capabilities.
Transfer learning is employed to decrease model training time.

Main Results:

The proposed algorithm demonstrates high recognition accuracy, reaching approximately 98.45%.
The method shows improved robustness and speed compared to existing finger vein recognition techniques.
The lightweight design makes the algorithm suitable for resource-constrained environments.

Conclusions:

The developed lightweight finger vein recognition algorithm offers a promising solution for biometric identification.
The combination of Gabor filters, network modification, and attention mechanisms effectively addresses common recognition problems.
The algorithm achieves superior performance in accuracy, robustness, and speed, validated by experimental results.