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@article{Oblak2025,

title = {Fingerprint image scale estimation for forensic identification systems},

author = {Tim Oblak and Jovana Videnović and Haris Kupinić and Vitomir Štruc and Peter Peer and Žiga Emeršič},

url = {https://univagora.ro/jour/index.php/ijccc/article/view/7031/2284},

doi = {https://doi.org/10.15837/ijccc.2025.2.7031},

year  = {2025},

date = {2025-04-02},

journal = {International Journal of Computers Communications & Control},

volume = {20},

issue = {2},

pages = {1--14},

abstract = {The large majority of modern software solutions intended for fingermark processing in a forensic context is heavily dependent on the correct image scaling. Fingermark images captured with digital cameras at a crime scene require the use of physical rulers or labels. While the resolution of a fingermark image can be calibrated manually by a forensic examiner in a lab, we propose an automated approach, which could be integrated directly into existing identification systems and would eliminate the need for human intervention. Our approach consists of a CNN regressor, which directly predicts the PPI of stochastically-sampled local patches based on the friction ridge information contained within. In a range of PPI between 500 and 1500, our method achieves a mean average error of around 24 PPI for fingerprint and fingermark images.},

keywords = {biometrics, finger marks, fingerprint recognition, fingerprints, latent fingerprints},

pubstate = {published},

tppubtype = {article}

}