IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913411277783040 |
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| author | Al-Ghadi, Musab Voerman, Joris Bakkali, Souhail Coustaty, Mickaël Sidere, Nicolas St-Georges, Xavier |
| author_facet | Al-Ghadi, Musab Voerman, Joris Bakkali, Souhail Coustaty, Mickaël Sidere, Nicolas St-Georges, Xavier |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_00573 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering Al-Ghadi, Musab Voerman, Joris Bakkali, Souhail Coustaty, Mickaël Sidere, Nicolas St-Georges, Xavier Computer Vision and Pattern Recognition 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. |
| title | IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.00573 |