While Identity Document Verification (IDV) technology on mobile devices
becomes ubiquitous in modern business operations, the risk of identity theft
and fraud is increasing. The identity document holder is normally required to
participate in an online video interview to circumvent impostors. However, the
current IDV process depends on an additional human workforce to support online
step-by-step guidance which is inefficient and expensive. The performance of
existing AI-based approaches cannot meet the real-time and lightweight demands
of mobile devices. In this paper, we address those challenges by designing an
edge intelligence-assisted approach for real-time IDV. Aiming at improving the
responsiveness of the IDV process, we propose a new document localization model
for mobile devices, LDRNet, to Localize the identity Document in Real-time. On
the basis of a lightweight backbone network, we build three prediction branches
for LDRNet, the corner points prediction, the line borders prediction and the
document classification. We design novel supplementary targets, the
equal-division points, and use a new loss function named Line Loss, to improve
the speed and accuracy of our approach. In addition to the IDV process, LDRNet
is an efficient and reliable document localization alternative for all kinds of
mobile applications. As a matter of proof, we compare the performance of LDRNet
with other popular approaches on localizing general document datasets. The
experimental results show that LDRNet runs at a speed up to 790 FPS which is
47x faster, while still achieving comparable Jaccard Index(JI) in single-model
and single-scale tests.

本文提出了基于边缘智能辅助的方法，设计了一个实时身份证验证（IDV）系统，LDRNet，用于在移动设备上本地化身份文件，增强 IDV 过程的响应性和准确率。