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arXiv · 2403.00573

IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering

Musab Al-Ghadi·Joris Voerman·Souhail Bakkali·Mickaël Coustaty·Nicolas Sidere·Xavier St-Georges
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Abstract

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.

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Musab Al-Ghadi, Joris Voerman, Souhail Bakkali, Mickaël Coustaty, Nicolas Sidere, Xavier St-Georges. 2024-03-01. IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering. https://arxiv.org/abs/2403.00573

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