2014 |
Peer, Peter; Emeršič, Žiga; Bule, Jernej; Žganec-Gros, Jerneja; Štruc, Vitomir Strategies for exploiting independent cloud implementations of biometric experts in multibiometric scenarios Journal Article In: Mathematical problems in engineering, vol. 2014, 2014. Abstract | Links | BibTeX | Tags: application, biometrics, cloud computing, face recognition, fingerprint recognition, fusion @article{peer2014strategies,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. |
Emeršič, Žiga; Bule, Jernej; Žganec-Gros, Jerneja; Štruc, Vitomir; Peer, Peter A case study on multi-modal biometrics in the cloud Journal Article In: Electrotechnical Review, vol. 81, no. 3, pp. 74, 2014. Abstract | Links | BibTeX | Tags: cloud, cloud computing, face recognition, face verification, fingerprint verification, fingerprints, fusion @article{emersic2014case,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. |
2013 |
Peer, Peter; Bule, Jernej; Gros, Jerneja Žganec; Štruc, Vitomir Building cloud-based biometric services Journal Article In: Informatica, vol. 37, no. 2, pp. 115, 2013. Abstract | Links | BibTeX | Tags: biometrics, cloud computing, development. SaaS, face recognition, fingerprint recognition @article{peer2013building,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. |