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Veins of Upper Limbs

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Finger Vein Verification on Different Datasets Based on Deep Learning with Triplet Loss.

Jun Li¹, Luokun Yang¹, Mingquan Ye^1,2

¹School of Medical Information, Wannan Medical College, Wuhu, Anhui 241002, China.

Computational and Mathematical Methods in Medicine

|October 31, 2022

Summary

Deep learning with triplet loss achieves high accuracy for finger vein verification. The model demonstrates excellent adaptability across diverse datasets, reaching 98% accuracy and AUC values above 0.98.

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Area of Science:

Biometrics
Machine Learning
Computer Vision

Background:

Finger vein recognition is a secure biometric authentication method.
Traditional methods face challenges in accuracy and robustness.

Purpose of the Study:

To develop and evaluate a deep learning model for finger vein verification.
To assess the model's performance across multiple datasets and training configurations.

Main Methods:

Utilized deep learning techniques combined with a triplet loss function.
Trained and validated the model on FV-USM, HKPU, and SDUMLA-HMT datasets.
Performed cross-validation between different dataset combinations.

Main Results:

Achieved a maximum accuracy of 98% for finger vein verification.
Reported Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values consistently above 0.98.
Demonstrated strong model adaptability and applicability due to cross-dataset validation.

Conclusions:

The deep learning model with triplet loss is highly effective for finger vein verification.
Training on more challenging datasets enhances model robustness and performance.
The model exhibits excellent generalization capabilities across different datasets.