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Statements

Subject Item
n2:RIV%2F00216208%3A11320%2F13%3A10173963%21RIV14-GA0-11320___
rdf:type
skos:Concept n16:Vysledek
rdfs:seeAlso
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6707101
dcterms:description
A growing availability of high-dimensional object data, e.g., from medicine or forensic analysis motivated us to develop a new variant of classical convolutional neural networks. The introduced model of N-dimensional convolutional neural networks (ND-CNN) enhanced with an enforced internal knowledge representation allows to process general N-dimensional object data while supporting adequate interpretation of the found object characteristics. Experimental results obtained so far for gender classification of 3D face scans confirm an extremely strong power of the proposed neural classifier. The developed ND-CNNs significantly outperformed humans (by 33%) while still allowing for a transparent representation of the face features present and detected in the data. A growing availability of high-dimensional object data, e.g., from medicine or forensic analysis motivated us to develop a new variant of classical convolutional neural networks. The introduced model of N-dimensional convolutional neural networks (ND-CNN) enhanced with an enforced internal knowledge representation allows to process general N-dimensional object data while supporting adequate interpretation of the found object characteristics. Experimental results obtained so far for gender classification of 3D face scans confirm an extremely strong power of the proposed neural classifier. The developed ND-CNNs significantly outperformed humans (by 33%) while still allowing for a transparent representation of the face features present and detected in the data.
dcterms:title
Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do? Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?
skos:prefLabel
Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do? Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?
skos:notation
RIV/00216208:11320/13:10173963!RIV14-GA0-11320___
n16:predkladatel
n17:orjk%3A11320
n3:aktivita
n18:P
n3:aktivity
P(GAP103/10/0783), P(GAP202/10/1333)
n3:cisloPeriodika
August 2013
n3:dodaniDat
n19:2014
n3:domaciTvurceVysledku
n8:7495838 n8:6306438 n8:6046797
n3:druhVysledku
n14:J
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
64178
n3:idVysledku
RIV/00216208:11320/13:10173963
n3:jazykVysledku
n21:eng
n3:klicovaSlova
sexual dimorphism; facial scans; classification; internal knowledge representation; convolutional neural networks
n3:klicoveSlovo
n7:convolutional%20neural%20networks n7:facial%20scans n7:internal%20knowledge%20representation n7:sexual%20dimorphism n7:classification
n3:kodStatuVydavatele
US - Spojené státy americké
n3:kontrolniKodProRIV
[2FA8A71E019B]
n3:nazevZdroje
Proceedings of The 2013 International Joint Conference on Neural Networks (IJCNN)
n3:obor
n4:BD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n11:GAP202%2F10%2F1333 n11:GAP103%2F10%2F0783
n3:rokUplatneniVysledku
n19:2013
n3:svazekPeriodika
2013
n3:tvurceVysledku
Mrázová, Iveta Pihera, Josef Velemínská, Jana
s:issn
2161-4407
s:numberOfPages
8
n20:doi
10.1109/IJCNN.2013.6707101
n15:organizacniJednotka
11320