Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86099115" target="_blank" >RIV/61989100:27240/15:86099115 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86099115

Výsledek na webu

<a href="http://dx.doi.org/10.1109/INCoS.2015.67" target="_blank" >http://dx.doi.org/10.1109/INCoS.2015.67</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/INCoS.2015.67" target="_blank" >10.1109/INCoS.2015.67</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles

Popis výsledku v původním jazyce

This paper proposes a human thermal face recognition approach with two variants based on Random linear Oracle (RLO) ensembles. For the two approaches, the Segmentation-based Fractal Texture Analysis (SFTA) algorithm was used for extracting features and the RLO ensemble classifier was used for recognizing the face from its thermal image. For the dimensionality reduction, one variant (SFTA-LDA-RLO) was used the technique of Linear Discriminant Analysis (LDA) while the other variant (SFTA-PCA-RLO) was used the Principal Component Analysis (PCA). The classifier's model was built using the RLO classifier during the training phase and in the testing phase then this model was used to identify the unknown sample images. The two variants were evaluated using the Terravic Facial IR Database and the experimental results showed that the two variants achieved a good recognition rate at 94.12% which is better than related work.

Název v anglickém jazyce

Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles

Popis výsledku anglicky

This paper proposes a human thermal face recognition approach with two variants based on Random linear Oracle (RLO) ensembles. For the two approaches, the Segmentation-based Fractal Texture Analysis (SFTA) algorithm was used for extracting features and the RLO ensemble classifier was used for recognizing the face from its thermal image. For the dimensionality reduction, one variant (SFTA-LDA-RLO) was used the technique of Linear Discriminant Analysis (LDA) while the other variant (SFTA-PCA-RLO) was used the Principal Component Analysis (PCA). The classifier's model was built using the RLO classifier during the training phase and in the testing phase then this model was used to identify the unknown sample images. The two variants were evaluated using the Terravic Facial IR Database and the experimental results showed that the two variants achieved a good recognition rate at 94.12% which is better than related work.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Intelligent Networking and Collaborative Systems INCoS-2015 : 7th International Conference : proceedings : September 2-4, 2015, Taipei, Tchaj-wan

ISBN

978-1-4673-7694-5

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

91-98

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Taipei

Datum konání akce

2. 9. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000380529500016