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Related Experiment Video

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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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Deep Learning-Based Wrist Vascular Biometric Recognition.

Felix Marattukalam¹, Waleed Abdulla¹, David Cole¹

¹Department of Electrical, Computer and Software Engineering, The University of Auckland, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)

|March 30, 2023

Summary

A novel deep learning system for contactless wrist vein biometrics was developed. This system achieves high accuracy in vein recognition and segmentation, offering a promising low-cost biometric solution.

Keywords:

Siamese Neural Network biometrics convolutional neural network deep learning machine learning wrist vein

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Area of Science:

Biometrics
Computer Vision
Deep Learning

Background:

Increasing demand for contactless biometric systems.
Deep learning excels in vein segmentation and matching.
Limited research exists on wrist vein biometrics compared to palm or finger veins.

Purpose of the Study:

To present a novel, low-cost, end-to-end contactless wrist vein biometric recognition system.
To utilize deep learning for effective wrist vein pattern extraction and matching.

Main Methods:

A novel U-Net Convolutional Neural Network (CNN) structure was trained on the FYO wrist vein dataset for vein segmentation.
A CNN and Siamese Neural Network were employed for wrist vein image matching.
System integration was achieved using a graphical user interface (GUI).

Main Results:

The U-Net CNN achieved a Dice Coefficient of 0.723 for wrist vein segmentation.
The matching subsystem obtained a highest F1-score of 84.7%.
The system demonstrated an average matching time of less than 3 seconds on a Raspberry Pi.

Conclusions:

The developed system provides an effective and low-cost solution for contactless wrist vein biometric recognition.
Deep learning techniques, specifically U-Net CNNs, are highly suitable for wrist vein segmentation and matching.
The system's efficiency and speed make it a viable option for real-world biometric applications.