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Parallel backpropagation neural network training for face recognition

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dc.citedby11
dc.contributor.authorOmarov B.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorTsoy A.en_US
dc.contributor.authorid57202103462en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid57192438194en_US
dc.date.accessioned2023-05-29T06:11:18Z
dc.date.available2023-05-29T06:11:18Z
dc.date.issued2016
dc.description.abstractIn this paper, we describe implementation of ANN training process using backpropagation learning algorithm for exploiting the high performance SIMD architecture of GPU using CUDA. We also compare sequential and parallel algorithm execution times and conducted speedup analysis for both the methods. The simulation results demonstrate a significant decrease on executing times and greater speedup than serial implementation of training and learning processes. All due to the parallel algorithm and use of the GPU, the training time for huge set of images get reduced significantly increasing the accuracy rate of face recognition. � 2016 Pushpa Publishing House, Allahabad, India.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.17654/EC016040801
dc.identifier.epage808
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85006333151
dc.identifier.spage801
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85006333151&doi=10.17654%2fEC016040801&partnerID=40&md5=0e50d96869b149ea664bd22663aff2ee
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22606
dc.identifier.volume16
dc.publisherPushpa Publishing Houseen_US
dc.sourceScopus
dc.sourcetitleFar East Journal of Electronics and Communications
dc.titleParallel backpropagation neural network training for face recognitionen_US
dc.typeArticleen_US
dspace.entity.typePublication
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