Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10173963" target="_blank" >RIV/00216208:11320/13:10173963 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6707101" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6707101</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/IJCNN.2013.6707101" target="_blank" >10.1109/IJCNN.2013.6707101</a>
Alternative languages
Result language
angličtina
Original language name
Can N-dimensional Convolutional Neural Networks Distinguish Men And Women Better Than Humans Do?
Original language 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 classificationof 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 thedata.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BD - Information theory
OECD FORD branch
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Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Proceedings of The 2013 International Joint Conference on Neural Networks (IJCNN)
ISSN
2161-4407
e-ISSN
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Volume of the periodical
2013
Issue of the periodical within the volume
August 2013
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
2833-2840
UT code for WoS article
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EID of the result in the Scopus database
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