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A comparative analysis of massive finger-vein recognition algorithms: from energy consumption perspective

dc.contributor.authorArgentina, Sebastián
dc.contributor.authorQuinteros, Axel
dc.contributor.authorHernández-García, Ruber
dc.contributor.authorFrati, Fernando Emmanuel
dc.contributor.authorBarrientos, Ricardo
dc.date.accessioned2023-03-03T13:22:20Z
dc.date.available2023-03-03T13:22:20Z
dc.date.issued2022
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/4460
dc.description.abstractThe volume of data generated has grown exponentially in recent years worldwide. In general, parallel architectures such as clusters, multicores, and GPUs have reduced processing times compared to their sequential version. However, in addition to the computational efficiency expressed in processing time, the scalability of the systems, the energy efficiency, and the price of the electrical energy consumed must be considered in parallel systems. This work aims to present an analysis of energy consumption between a GPU and a multicore platform, using the same exhaustive search algorithm as a case study to solve kNN queries in metric spaces on a database of finger vein images. The experimentation was performed on a database of 4,000,000 finger vein images on multicore and GPU platforms. The experimental results show that the GPU platform is 2.52 times lower than the multicore platform in dissipated energy when solving 56 simultaneous queries. The conducted study is pioneering in this kind of analysis on massive human recognition tasks.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceProceedings - International Conference of the Chilean Computer Science Society, SCCC, 2022, 1-6es_CL
dc.subjectGraphics processing unitses_CL
dc.subjectMulticore processinges_CL
dc.subjectSoftwarees_CL
dc.subjectImage resolutiones_CL
dc.subjectVeinses_CL
dc.subjectHardwarees_CL
dc.subjectFingerses_CL
dc.titleA comparative analysis of massive finger-vein recognition algorithms: from energy consumption perspectivees_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.uriieeexplore.ieee.org/document/10000304es_CL
dc.ucm.doidoi.org/10.1109/SCCC57464.2022.10000304es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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