Recognition of Face Images with Noise Based on Tucker Decomposition
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096032" target="_blank" >RIV/61989100:27240/15:86096032 - isvavai.cz</a>
Result on the web
<a href="http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7379595" target="_blank" >http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7379595</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SMC.2015.463" target="_blank" >10.1109/SMC.2015.463</a>
Alternative languages
Result language
angličtina
Original language name
Recognition of Face Images with Noise Based on Tucker Decomposition
Original language description
The main goal of this paper is to detect faces from noisy images using three different classification methods and compare the results obtained from the classification methods. The faces are described by a set of images. Many other unsupervised statistical algorithms such as Principal Component Analysis (PCA) or Singular Value Decomposition (SVD) use only one image per person to extract features from the face. These approaches can lose important information, for example a relationship between images of the same person taken under different conditions. It shows that data structure like tensor and it decomposition increase the quality of recognition in this task because it better captures important features of one face taken from several images. The accuracy of the tensor approach is compared with other well-known techniques such as Support Vector Machine (SVM) and Neural Network (NN).
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2015 IEEE International Conference On Systems, Man And Cybernetics (Smc 2015) : Big Data Analytics For Human-Centric Systems
ISBN
978-1-4799-8696-5
ISSN
1062-922X
e-ISSN
—
Number of pages
5
Pages from-to
2649-2653
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Hong Kong
Event date
Oct 9, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000368940202129