TeLL Me what you cant see
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909551696019456 |
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| author | Cavasin, Saverio Biasetton, Pietro Tamiazzo, Mattia Conti, Mauro Milani, Simone |
| author_facet | Cavasin, Saverio Biasetton, Pietro Tamiazzo, Mattia Conti, Mauro Milani, Simone |
| contents | During criminal investigations, images of persons of interest directly influence the success of identification procedures. However, law enforcement agencies often face challenges related to the scarcity of high-quality images or their obsolescence, which can affect the accuracy and success of people searching processes. This paper introduces a novel forensic mugshot augmentation framework aimed at addressing these limitations. Our approach enhances the identification probability of individuals by generating additional, high-quality images through customizable data augmentation techniques, while maintaining the biometric integrity and consistency of the original data. Several experimental results show that our method significantly improves identification accuracy and robustness across various forensic scenarios, demonstrating its effectiveness as a trustworthy tool law enforcement applications. Index Terms: Digital Forensics, Person re-identification, Feature extraction, Data augmentation, Visual-Language models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19478 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TeLL Me what you cant see Cavasin, Saverio Biasetton, Pietro Tamiazzo, Mattia Conti, Mauro Milani, Simone Computer Vision and Pattern Recognition Computers and Society I.2.1 During criminal investigations, images of persons of interest directly influence the success of identification procedures. However, law enforcement agencies often face challenges related to the scarcity of high-quality images or their obsolescence, which can affect the accuracy and success of people searching processes. This paper introduces a novel forensic mugshot augmentation framework aimed at addressing these limitations. Our approach enhances the identification probability of individuals by generating additional, high-quality images through customizable data augmentation techniques, while maintaining the biometric integrity and consistency of the original data. Several experimental results show that our method significantly improves identification accuracy and robustness across various forensic scenarios, demonstrating its effectiveness as a trustworthy tool law enforcement applications. Index Terms: Digital Forensics, Person re-identification, Feature extraction, Data augmentation, Visual-Language models. |
| title | TeLL Me what you cant see |
| topic | Computer Vision and Pattern Recognition Computers and Society I.2.1 |
| url | https://arxiv.org/abs/2503.19478 |