Recognition of Face Images with Noise Based on Tucker Decomposition

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Recognition of Face Images with Noise Based on Tucker Decomposition

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

Recognition of Face Images with Noise Based on Tucker Decomposition

Popis výsledku anglicky

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).

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

Počet stran výsledku

5

Strana od-do

2649-2653

Název nakladatele

IEEE Computer Society

Místo vydání

Los Alamitos

Místo konání akce

Hong Kong

Datum konání akce

9. 10. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000368940202129