IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering

• URL: http://arxiv.org/abs/2403.00573v1
• Date: Fri, 1 Mar 2024 14:53:31 GMT
• Title: IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering
• Authors: Musab Al-Ghadi, Joris Voerman, Souhail Bakkali, Micka\"el Coustaty, Nicolas Sidere, Xavier St-Georges
• Abstract summary: IDTrust is a system that enhances the quality of identification documents by using a deep learning-based approach. By utilizing a bandpass filtering-based method, the system aims to effectively detect and differentiate ID quality.
• Score: 0.5542462410129538
• License: http://creativecommons.org/licenses/by/4.0/
• 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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