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Related Experiment Video

Updated: Jul 2, 2025

Super-resolution Imaging of Neuronal Dense-core Vesicles

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

Finger Vein Identification Based on Large Kernel Convolution and Attention Mechanism.

Meihui Li^1,2, Yufei Gong³, Zhaohui Zheng^1,2

¹School of Computer Science and Technology, Soochow University, Suzhou 215006, China.

Sensors (Basel, Switzerland)

|February 24, 2024

Summary

Let-Net, a novel finger vein (FV) identification method, uses large kernels and attention mechanisms to improve accuracy. This biometric technology offers efficient and accurate identity authentication with low computational cost.

Keywords:

CNN attention mechanism dual-channel finger vein identification large kernel

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Area of Science:

Biometrics
Computer Vision
Machine Learning

Background:

Finger vein (FV) identification is a biometric technology for identity authentication.
Existing Convolutional Neural Network (CNN) methods face limitations due to small receptive fields and difficulty capturing long-range dependencies.

Purpose of the Study:

To introduce Let-Net (large kernel and attention mechanism network) for enhanced FV identification.
To address the limitations of CNN-based FV identification by integrating local and global information.

Main Methods:

Utilized large kernels in deep convolution with residual connections to capture broad spatial context and reduce model parameters.
Integrated an attention mechanism to enhance information flow across channel and spatial dimensions for global information modeling.

Main Results:

Achieved excellent identification performance across nine public datasets.
On the FV_USM dataset, Let-Net reached an Equal Error Rate (EER) of 0.04% and an accuracy rate of 99.77%.
The model has a low parameter count (0.89M) and FLOPs (0.25G), indicating efficient training and inference.

Conclusions:

Let-Net effectively extracts crucial FV features by combining local and global information.
The method demonstrates superior performance and efficiency, facilitating easier deployment in various applications.