Title: Finger vein recognition based on efficient channel attention and GhostNet

Authors: Yintao Ke; Hui Zheng; Jing Jie; Beiping Hou; Yuchuan Chen

Addresses: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China ' School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, Zhejiang, China

Abstract: The deep convolutional network (DCN) suffers from drawbacks such as high computational complexity and slow speed. To address these issues and facilitate the deployment of DCNs in embedded devices, we propose a finger vein recognition method based on lightweight Efficient Channel Attention (ECA) mechanism and GhostNet. By combining the ECA mechanism with the GhostNet's G-bneck, we create a new module called ECAGhostNet. Additionally, we establish a more realistic FV-UST dataset for finger vein door locks which includes images with various challenges like rotation, stains, skin damage, hand sweat, temperature variations, and illumination differences. Experimental results demonstrate that ECAGhostNet outperforms GhostNet on the public FV-USM dataset, improving accuracy by 0.82% with minimal parameter increase (1.9 M). On the self-built FV-UST dataset, ECAGhostNet achieves a 0.63% accuracy improvement over GhostNet. Furthermore, we validate the effectiveness of our proposed model on the Jetson Nano device, confirming its suitability for real-world embedded applications.

Keywords: deep convolutional neural network; efficient channel attention mechanism; finger vein recognition; GhostNet.

DOI: 10.1504/IJWMC.2024.137172

International Journal of Wireless and Mobile Computing, 2024 Vol.26 No.2, pp.157 - 167

Received: 14 Mar 2023
Accepted: 24 Jul 2023
Published online: 04 Mar 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article