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Finger vein recognition method based on ant colony optimization and improved EfficientNetV2.

Xiao Ma¹, Xuemei Luo¹

¹School of Electrical Engineering, Guizhou University, Guiyang 550025, China.

Mathematical Biosciences and Engineering : MBE

|June 16, 2023

Summary

This summary is machine-generated.

This study introduces an improved deep learning method for finger vein recognition, enhancing accuracy by integrating ant colony optimization with EfficientNetV2 and a dual attention fusion network. The novel approach achieves a 98.96% recognition rate, outperforming existing models.

Keywords:

DANet EfficientNetV2 ROI extraction ant colony optimization finger vein recognition

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Area of Science:

Biometrics
Computer Vision
Artificial Intelligence

Background:

Deep learning, particularly Convolutional Neural Networks (CNNs), is pivotal in image recognition.
Finger vein recognition is a growing research area within biometrics, leveraging unique vein patterns.
Existing methods face challenges with image noise, robustness, and cross-domain generalization.

Purpose of the Study:

To develop a robust and accurate finger vein recognition system.
To address limitations of current deep learning models in practical applications.
To improve the performance of finger vein recognition using novel algorithmic combinations.

Main Methods:

Proposed a novel finger vein recognition method combining ant colony optimization (ACO) and an improved EfficientNetV2 architecture.
Utilized ACO for optimizing Region of Interest (ROI) extraction in finger vein images.
Integrated a dual attention fusion network (DANet) with EfficientNetV2 for enhanced feature extraction.

Main Results:

The proposed method achieved a high recognition rate of 98.96% on the FV-USM dataset.
Experimental results demonstrated superior performance compared to other existing algorithmic models.
The method showed effectiveness in handling interference and noise in finger vein images.

Conclusions:

The developed finger vein recognition method demonstrates significant potential for practical applications.
The integration of ACO and DANet with EfficientNetV2 offers a promising direction for enhancing biometric security.
The study validates the effectiveness of the proposed approach in achieving high accuracy and robustness.