Rate-energy-accuracy Optimization Of Convolutional Architectures For Face Recognition

dc.creatorBondi
dc.creatorL; Baroffio
dc.creatorL; Cesana
dc.creatorM; Tagliasacchi
dc.creatorM; Chiachia
dc.creatorG; Rocha
dc.creatorA
dc.date2016
dc.date2016-12-06T18:30:59Z
dc.date2016-12-06T18:30:59Z
dc.date.accessioned2018-03-29T02:03:36Z
dc.date.available2018-03-29T02:03:36Z
dc.identifier1095-9076
dc.identifierJournal Of Visual Communication And Image Representation. ACADEMIC PRESS INC ELSEVIER SCIENCE, n. 36, p. 142 - 148.
dc.identifier1047-3203
dc.identifierWOS:000371280200012
dc.identifier10.1016/j.jvcir.2015.12.015
dc.identifierhttp://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S1047320315002552
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/320184
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1310950
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFace recognition systems based on Convolutional Neural Networks (CNNs) or convolutional architectures currently represent the state of the art, achieving an accuracy comparable to that of humans. Nonetheless, there are two issues that might hinder their adoption on distributed battery-operated devices (e.g., visual sensor nodes, smartphones, and wearable devices). First, convolutional architectures are usually computationally demanding, especially when the depth of the network is increased to maximize accuracy. Second, transmitting the output features produced by a CNN might require a bitrate higher than the one needed for coding the input image. Therefore, in this paper we address the problem of optimizing the energy-rate-accuracy characteristics of a convolutional architecture for face recognition. We carefully profile a CNN implementation on a Raspberry Pi device and optimize the structure of the neural network, achieving a 17-fold speedup without significantly affecting recognition accuracy. Moreover, we propose a coding architecture custom-tailored to features extracted by such model. (C) 2015 Elsevier Inc. All rights reserved.
dc.description36
dc.description
dc.description142
dc.description148
dc.descriptionFuture and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open Grant [296676]
dc.descriptionBrazilian National Research Council (CNPq)
dc.descriptionBrazilian Coordination for the Improvement of Higher Education Personnel (CAPES), through the DeepEyes project
dc.descriptionSao Paulo Research Foundation (FAPESP) [2013/11359-0]
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description
dc.description
dc.description
dc.languageEnglish
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE
dc.publisherSAN DIEGO
dc.relationJournal of Visual Communication and Image Representation
dc.rightsfechado
dc.sourceWOS
dc.subjectConvolutional Architectures
dc.subjectConvolutional Neural Networks (cnns)
dc.subjectOptimization
dc.subjectCoding
dc.subjectFace Recognition
dc.subjectAnalyze-then-compress (atc)
dc.subjectDeep Learning
dc.subjectDeep Neural Networks
dc.titleRate-energy-accuracy Optimization Of Convolutional Architectures For Face Recognition
dc.typeArtículos de revistas


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