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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}

}

@article{peer2014strategies,

title = {Strategies for exploiting independent cloud implementations of biometric experts in multibiometric scenarios},

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

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

doi = {http://dx.doi.org/10.1155/2014/585139},

year  = {2014},

date = {2014-01-01},

urldate = {2014-01-01},

journal = {Mathematical problems in engineering},

volume = {2014},

publisher = {Hindawi Publishing Corporation},

abstract = {Cloud computing represents one of the fastest growing areas of technology and offers a new computing model for various applications and services. This model is particularly interesting for the area of biometric recognition, where scalability, processing power, and storage requirements are becoming a bigger and bigger issue with each new generation of recognition technology. Next to the availability of computing resources, another important aspect of cloud computing with respect to biometrics is accessibility. Since biometric cloud services are easily accessible, it is possible to combine different existing implementations and design new multibiometric services that next to almost unlimited resources also offer superior recognition performance and, consequently, ensure improved security to its client applications. Unfortunately, the literature on the best strategies of how to combine existing implementations of cloud-based biometric experts into a multibiometric service is virtually nonexistent. In this paper, we try to close this gap and evaluate different strategies for combining existing biometric experts into a multibiometric cloud service. We analyze the (fusion) strategies from different perspectives such as performance gains, training complexity, or resource consumption and present results and findings important to software developers and other researchers working in the areas of biometrics and cloud computing. The analysis is conducted based on two biometric cloud services, which are also presented in the paper.},

keywords = {application, biometrics, cloud computing, face recognition, fingerprint recognition, fusion},

pubstate = {published},

tppubtype = {article}

}

@article{peer2013building,

title = {Building cloud-based biometric services},

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

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

year  = {2013},

date = {2013-01-01},

urldate = {2013-01-01},

journal = {Informatica},

volume = {37},

number = {2},

pages = {115},

publisher = {Slovenian Society Informatika/Slovensko drustvo Informatika},

abstract = {Over the next few years the amount of biometric data being at the disposal of various agencies and authentication service providers is expected to grow significantly. Such quantities of data require not only enormous amounts of storage but unprecedented processing power as well. To be able to face this future challenges more and more people are looking towards cloud computing, which can address these challenges quite effectively with its seemingly unlimited storage capacity, rapid data distribution and parallel processing capabilities. Since the available literature on how to implement cloud-based biometric services is extremely scarce, this paper capitalizes on the most important challenges encountered during the development work on biometric services, presents the most important standards and recommendations pertaining to biometric services in the cloud and ultimately, elaborates on the potential value of cloud-based biometric solutions by presenting a few existing (commercial) examples. In the final part of the paper, a case study on fingerprint recognition in the cloud and its integration into the e-learning environment Moodle is presented.},

keywords = {biometrics, cloud computing, development. SaaS, face recognition, fingerprint recognition},

pubstate = {published},

tppubtype = {article}

}