Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F14%3A00428536" target="_blank" >RIV/67985556:_____/14:00428536 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.dsp.2014.04.008" target="_blank" >http://dx.doi.org/10.1016/j.dsp.2014.04.008</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.dsp.2014.04.008" target="_blank" >10.1016/j.dsp.2014.04.008</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

Popis výsledku v původním jazyce

This study proposes a novel near infrared face recognition algorithm based on a combination of both local and global features. In this method local features are extracted from partitioned images by means of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then used to reduce the dimension of features. In order to make full use of global and local features and further improve the performance, a decision fusion technique is employed by using weighted sum rule. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed method has superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time is acceptable for on-line face recognition systems.

Název v anglickém jazyce

Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

Popis výsledku anglicky

This study proposes a novel near infrared face recognition algorithm based on a combination of both local and global features. In this method local features are extracted from partitioned images by means of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then used to reduce the dimension of features. In order to make full use of global and local features and further improve the performance, a decision fusion technique is employed by using weighted sum rule. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed method has superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time is acceptable for on-line face recognition systems.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Digital Signal Processing

ISSN

1051-2004

e-ISSN

—

Svazek periodika

31

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

27

Strana od-do

13-27

Kód UT WoS článku

000337267200002

EID výsledku v databázi Scopus

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