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Related Experiment Video

Updated: Aug 12, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Published on: March 28, 2025

A deep ensemble learning method for single finger-vein identification.

Chongwen Liu^1,2, Huafeng Qin^1,2, Qun Song^1,2

¹College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China.

Frontiers in Neurorobotics

|January 30, 2023

Summary

This summary is machine-generated.

This study introduces a novel deep ensemble learning method for single sample per person (SSPP) finger-vein recognition. The approach effectively extracts robust features from limited data, outperforming existing methods in personal verification.

Keywords:

deep learning ensemble learning finger-vein recognition pattern recognition single sample per person

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Area of Science:

Biometrics
Computer Science
Machine Learning

Background:

Finger-vein biometrics is crucial for personal verification.
Single sample per person (SSPP) recognition remains a challenge due to limited training data.
Current deep learning methods struggle with robust feature extraction from single images.

Purpose of the Study:

To develop a robust finger-vein recognition method for the SSPP scenario.
To address the limitations of existing deep learning approaches in feature extraction with limited data.
To enhance the accuracy and reliability of personal verification using finger-vein patterns.

Main Methods:

A deep ensemble learning method is proposed, utilizing multiple independent deep learning classifiers.
Feature maps are generated from input finger-vein images.
A shared learning scheme and adjusted learning speeds for weak classifiers are investigated to improve feature representation and performance.

Main Results:

The proposed deep ensemble model demonstrates superior performance in SSPP finger-vein recognition.
The method effectively extracts robust and discriminative features even with a single training sample.
Experimental results on two public databases show a distinct advantage over popular existing solutions.

Conclusions:

The proposed deep ensemble learning method offers a robust solution for SSPP finger-vein recognition.
This approach overcomes the data dependency of traditional deep learning models in biometrics.
The findings pave the way for more efficient and reliable personal verification systems using finger-vein biometrics.