IDTrust is a system developed to enhance the quality of identification documents by utilizing a deep learning-based approach. It aims to effectively detect and differentiate ID quality using bandpass filtering. The system offers significant improvements in dataset applicability by eliminating the need for relying on original document patterns for quality checks. By conducting experiments on datasets like MIDV-2020 and L3i-ID, optimal parameters were identified, significantly improving discrimination performance between original and scanned ID documents. The proposed models, DeepQD and GuidedDeepQD, outperform existing methods in accurately discerning between original and scanned IDs across different countries. They achieve near-perfect accuracy, F1-score, and AUC values, showcasing robust discrimination capabilities.
他の言語に翻訳
原文コンテンツから
arxiv.org
深掘り質問