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.
Publisher
MDPI AG
Type
journal article