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Veins of Upper Limbs

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The human circulatory system, a marvel of biological engineering, is a complex network of vessels that transport blood throughout the body. Among these, the veins responsible for carrying blood from the upper limbs are divided into two categories: deep and superficial.
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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

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Efficient multi-finger vein recognition using layer-wise progressive MobileNet fine-tuning and a Dense-Head

Alaa S Alaerjan¹, Ayman Mohamed Mostafa², Alshimaa Abdelraof Mahmoud³

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, 72388, Sakaka, Saudi Arabia.

Scientific Reports

|December 18, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep learning framework for finger-vein recognition, enabling multi-finger authentication on resource-limited devices. The efficient system achieves state-of-the-art accuracy, overcoming previous deployment challenges.

Keywords:

Biometric authentication Finger-vein recognition Layer-wise progressive fine-tuning Lightweight deep learning MobileNet

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Area of Science:

Biometrics and Pattern Recognition
Deep Learning for Security
Embedded Systems Security

Background:

Finger-vein recognition is a secure biometric modality but faces challenges in computational cost and single-finger limitations for resource-constrained devices.
Existing deep learning models for finger-vein recognition are often computationally intensive and lack flexibility in enrollment.
The rigidity of single-finger enrollment restricts user convenience and system adaptability.

Purpose of the Study:

To develop a computationally efficient and flexible deep learning framework for finger-vein recognition.
To enable multi-finger authentication on resource-limited devices without compromising accuracy or speed.
To address the challenges of high computational cost and single-finger enrollment in current finger-vein systems.

Main Methods:

A two-stage deep learning framework combining a lightweight, adaptive MobileNet feature extractor with a Dense-Head Probabilistic Siamese (DHPS) matcher.
Layer-wise unfreezing technique to optimize the feature extractor for a balance between model compactness and discriminative power.
DHPS matcher utilizing calibrated probabilistic outputs optimized via binary cross-entropy, replacing traditional margin-based losses.

Main Results:

Achieved state-of-the-art performance on three public finger-vein datasets (FV-USM, UTFVP, VERA) with low Equal Error Rates (EER) of 0.002, 0.067, and 0.075.
Obtained high F1-scores of 99.8%, 95.6%, and 91.3% on the respective test sets, demonstrating robust accuracy.
The compact model facilitates fast inference, making it suitable for embedded platforms.

Conclusions:

The proposed framework significantly enhances the accuracy, flexibility, and efficiency of finger-vein biometrics.
This advancement overcomes major obstacles for real-world deployment, particularly on embedded systems.
The pre-trained feature extractor will be publicly released to foster further research in efficient biometric systems.