Singular Value Decomposition and Principal Component Analysis in Face Images Recognition and FSVDR of Faces

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50014616" target="_blank" >RIV/62690094:18450/19:50014616 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-99996-8_10" target="_blank" >http://dx.doi.org/10.1007/978-3-319-99996-8_10</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-99996-8_10" target="_blank" >10.1007/978-3-319-99996-8_10</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Singular Value Decomposition and Principal Component Analysis in Face Images Recognition and FSVDR of Faces

Popis výsledku v původním jazyce

The singular value decomposition (SVD) is an important tool for matrix computations with various uses. It is often combined with other methods or used within specific procedures. The text briefly introduces the SVD and lists its important features and selected elements of the SVD theory. In addition, the text deals with two important issues related to the field of artificial intelligence with extensive practical use. The first is face recognition analysis in relation to face representation using principal component analysis (PCA) and the second is fractional order singular value decomposition representation (FSVDR) of faces. The presented procedures can be used in an efficient real-time face recognition system, which can identify a subject’s head and then perform a recognition task by comparing the face to those of known individuals. The essence of the procedures, way of their application, their advantages and shortcomings, and selected results are presented in the text. All procedures are implemented in MATLAB software.

Název v anglickém jazyce

Singular Value Decomposition and Principal Component Analysis in Face Images Recognition and FSVDR of Faces

Popis výsledku anglicky

The singular value decomposition (SVD) is an important tool for matrix computations with various uses. It is often combined with other methods or used within specific procedures. The text briefly introduces the SVD and lists its important features and selected elements of the SVD theory. In addition, the text deals with two important issues related to the field of artificial intelligence with extensive practical use. The first is face recognition analysis in relation to face representation using principal component analysis (PCA) and the second is fractional order singular value decomposition representation (FSVDR) of faces. The presented procedures can be used in an efficient real-time face recognition system, which can identify a subject’s head and then perform a recognition task by comparing the face to those of known individuals. The essence of the procedures, way of their application, their advantages and shortcomings, and selected results are presented in the text. All procedures are implemented in MATLAB software.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Information Systems Architecture and Technology: Proceedings of 39th International Conference on Information Systems Architecture and Technology – ISAT 2018

ISBN

978-3-319-99995-1

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

10

Strana od-do

105-114

Název nakladatele

Springer Nature Switzerland AG Gewerbestrasse 11, 6330 Cham, Switzerland

Místo vydání

Gewerbestrasse 11, 6330 Cham, Switzerland

Místo konání akce

Polsko

Datum konání akce

16. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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