Multi-Source Fusion User Authentication Based on Channel and Image Features
Aiwen Wang, Jie Tang, Hongyu Luo, Hong Wen, Pin Han-Ho, Shih Yu Chang · 2023
This paper aims to address the real-time transmission of image information verification in industrial operations through the fusion of multi-source information. It combines channel and image feature extraction, employing a neural network model based on self-supervised learning for deep feature learning. The model utilizes channel data collection, image data processing, and multiple convolutions to extract profound physical features, achieving a more accurate reflection of user environmental characteristics. Experimental results indicate that compared to traditional unsupervised learning and single-feature verification approaches, the proposed solution in this paper achieves a higher success rate in verification within industrial settings.