Rotation and Noise Invariant Near-Infrared Face Recognition by means of Zernike Moments and Spectral Regression Discriminant Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F13%3A00390234" target="_blank" >RIV/67985556:_____/13:00390234 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1117/1.JEI.22.1.013030" target="_blank" >http://dx.doi.org/10.1117/1.JEI.22.1.013030</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/1.JEI.22.1.013030" target="_blank" >10.1117/1.JEI.22.1.013030</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rotation and Noise Invariant Near-Infrared Face Recognition by means of Zernike Moments and Spectral Regression Discriminant Analysis

Popis výsledku v původním jazyce

Face recognition is a rapidly growing research area, which is based heavily on the methods of machine learning, computer vision, and image processing.We propose a rotation and noise invariant near-infrared face-recognition system using an orthogonal invariant moment, namely, Zernike moments (ZMs) as a feature extractor in the near-infrared domain and spectral regression discriminant analysis (SRDA) as an efficient algorithm to decrease the computational complexity of the system, enhance the discrimination power of features, and solve the ?small sample size problem simultaneously. Experimental results based on the CASIA NIR database show the noise robustness and rotation invariance of the proposed approach. Further analysis shows that SRDA as a sophisticated technique, improves the accuracy and time complexity of the system compared with other data reduction methods such as linear discriminant analysis.

Název v anglickém jazyce

Rotation and Noise Invariant Near-Infrared Face Recognition by means of Zernike Moments and Spectral Regression Discriminant Analysis

Popis výsledku anglicky

Face recognition is a rapidly growing research area, which is based heavily on the methods of machine learning, computer vision, and image processing.We propose a rotation and noise invariant near-infrared face-recognition system using an orthogonal invariant moment, namely, Zernike moments (ZMs) as a feature extractor in the near-infrared domain and spectral regression discriminant analysis (SRDA) as an efficient algorithm to decrease the computational complexity of the system, enhance the discrimination power of features, and solve the ?small sample size problem simultaneously. Experimental results based on the CASIA NIR database show the noise robustness and rotation invariance of the proposed approach. Further analysis shows that SRDA as a sophisticated technique, improves the accuracy and time complexity of the system compared with other data reduction methods such as linear discriminant analysis.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F11%2F1552" target="_blank" >GAP103/11/1552: Momenty a momentové invarianty v analýze obrazu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

Journal of Electronic Imaging

ISSN

1017-9909

e-ISSN

—

Svazek periodika

22

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

1-11

Kód UT WoS článku

000322079000003

EID výsledku v databázi Scopus

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