Objave

@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}

}

@article{emersic2014case,

title = {A case study on multi-modal biometrics in the cloud},

author = {Žiga Emeršič and Jernej Bule and Jerneja Žganec-Gros and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/en/acasestudyonmulti-modalbiometricsinthecloud/},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Electrotechnical Review},

volume = {81},

number = {3},

pages = {74},

publisher = {Elektrotehniski Vestnik},

abstract = {Cloud computing is particularly interesting for the area of biometric recognition, where scalability, availability and accessibility are important aspects. In this paper we try to evaluate different strategies for combining existing uni-modal (cloud-based) biometric experts into a multi-biometric cloud-service. We analyze several fusion strategies from the perspective of performance gains, training complexity and resource consumption and discuss the results of our analysis. The experimental evaluation is conducted based on two biometric cloud-services developed in the scope of the Competence Centere CLASS, a face recognition service and a fingerprint recognition service, which are also briefly described in the paper. The presented results are important to researchers and developers working in the area of biometric services for the cloud looking for easy solutions for improving the quality of their services.},

keywords = {cloud, cloud computing, face recognition, face verification, fingerprint verification, fingerprints, fusion},

pubstate = {published},

tppubtype = {article}

}