Zero-FVeinNet: Optimizing Finger Vein Recognition with Shallow CNNs and Zero-Shuffle Attention for Low-Computational Devices
Journal
Electronics (Switzerland)
Journal Volume
13
Journal Issue
9
Start Page
1751
ISSN
2079-9292
Date Issued
2024
Author(s)
Nghi C. Tran
Bach-Tung Pham
Kuo-Chen Li
Phuong Thi Le
Shih-Lun Chen
Aufaclav Zatu Kusuma Frisky
Yung-Hui Li
Jia-Ching Wang
DOI
10.3390/electronics13091751
Abstract
In the context of increasing reliance on mobile devices, robust personal security solutions are critical. This paper presents Zero-FVeinNet, an innovative, lightweight convolutional neural network (CNN) tailored for finger vein recognition on mobile and embedded devices, which are typically resource-constrained. The model integrates cutting-edge features such as Zero-Shuffle Coordinate Attention and a blur pool layer, enhancing architectural efficiency and recognition accuracy under various imaging conditions. A notable reduction in computational demands is achieved through an optimized design involving only 0.3 M parameters, thereby enabling faster processing and reduced energy consumption, which is essential for mobile applications. An empirical evaluation on several leading public finger vein datasets demonstrates that Zero-FVeinNet not only outperforms traditional biometric systems in speed and efficiency but also establishes new standards in biometric identity verification. The Zero-FVeinNet achieves a Correct Identification Rate (CIR) of 99.9% on the FV-USM dataset, with a similarly high accuracy on other datasets. This paper underscores the potential of Zero-FVeinNet to significantly enhance security features on mobile devices by merging high accuracy with operational efficiency, paving the way for advanced biometric verification technologies.
SDGs
Publisher
MDPI AG
Type
journal article