IDTrust: Deep Identity Document Quality Detection with Bandpass
  Filtering
Musab Al-Ghadi Joris Voerman Souhail Bakkali Mickaël Coustaty Nicolas Sidere Xavier St-Georges
The increasing use of digital technologies and mobile-based registration
procedures highlights the vital role of personal identity documents (IDs) in
verifying users and safeguarding sensitive information. However, the rise in
counterfeit ID production poses a significant challenge, necessitating the
development of reliable and efficient automated verification methods. This
paper introduces IDTrust, a deep-learning framework for assessing the quality
of IDs. IDTrust is a system that enhances the quality of identification
documents by using a deep learning-based approach. This method eliminates the
need for relying on original document patterns for quality checks and
pre-processing steps for alignment. As a result, it offers significant
improvements in terms of dataset applicability. By utilizing a bandpass
filtering-based method, the system aims to effectively detect and differentiate
ID quality. Comprehensive experiments on the MIDV-2020 and L3i-ID datasets
identify optimal parameters, significantly improving discrimination performance
and effectively distinguishing between original and scanned ID documents.