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dc.contributor.authorHerrera Triguero, Francisco 
dc.contributor.authorTabik, Siham
dc.contributor.authorSánchez, Andrés J.
dc.date.accessioned2020-09-28T07:31:00Z
dc.date.available2020-09-28T07:31:00Z
dc.date.issued2020
dc.identifier.citationSanchez-Fernandez, A. J., Romero, L. F., Peralta, D., Medina-Pérez, M. A., Saeys, Y., Herrera, F., & Tabik, S. (2020). Asynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systems. IEEE Access, 8, 124236-124253. [DOI: 10.1109/ACCESS.2020.3005476]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/63583
dc.description.abstractLatent fingerprint identification is one of the most essential identification procedures in criminal investigations. Addressing this task is challenging as (i) it requires analyzing massive databases in reasonable periods and (ii) it is commonly solved by combining different methods with very complex datadependencies, which make fully exploiting heterogeneous CPU-GPU systems very complex. Most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time. Indeed, the most accurate approach was designed for one single thread. This work introduces ALFI (Asynchronous processing for Latent Fingerprint Identification), the fastest methodology for latent fingerprint identification maintaining high accuracy. ALFI fully exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism for analyzing massive databases. Our approach reduces idle times in processing and fully exploits the inherent parallelism of comparing latent fingerprints to fingerprint impressions. We analyzed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results reveal that ALFI is in average 22x faster than the state-of-the-art algorithm, reaching a value of 44.7x for the best-studied case.es_ES
dc.description.sponsorshipSpanish Government TIN2016-80920-Res_ES
dc.description.sponsorshipSpanish Government TIN2016-80920-Res_ES
dc.description.sponsorshipUniversity of Malaga through the U-Smart-Drive projectes_ES
dc.description.sponsorshipI Plan Propio de Investigaciones_ES
dc.description.sponsorshipPostdoctoral Fellowship of the Research Foundation of Flanders (FWO) 170303/12X1619Nes_ES
dc.description.sponsorshipISAC Marylou Ingram Scholares_ES
dc.description.sponsorshipSpanish Government RYC-2015-18136es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)es_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectAsynchronous processinges_ES
dc.subjectAccelerator architectureses_ES
dc.subjectCUDAes_ES
dc.subjectFine-grained parallelismes_ES
dc.subjectFingerprint recognitiones_ES
dc.subjectHeterogeneous computinges_ES
dc.subjectLatent fingerprint identificationes_ES
dc.subjectParallel processinges_ES
dc.titleAsynchronous Processing for Latent Fingerprint Identification on Heterogeneous CPU-GPU Systemses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1109/ACCESS.2020.3005476


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España