A deep learning approach for facial emotions recognition using principal component analysis and neural network techniques

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50020172" target="_blank" >RIV/62690094:18470/22:50020172 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/epdf/10.1111/phor.12426" target="_blank" >https://onlinelibrary.wiley.com/doi/epdf/10.1111/phor.12426</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/phor.12426" target="_blank" >10.1111/phor.12426</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A deep learning approach for facial emotions recognition using principal component analysis and neural network techniques

Popis výsledku v původním jazyce

In this work, advanced facial emotions are recognized using Neural network-based (NN) PCA methodology. The earlier models are cannot detect facial emotions with moving conditions but the CCTV and other advanced applications are mostly depending on moving object-based emotion recognition. The blurring, mask, and moving object-based facial image are applied to the training process, and at the testing condition, real-time facial images are applied. The PCA is extracting features and pre-processing the images with NN deep learning process. The proposed facial emotion recognition model is most useful for advanced applications. The design is finally verified through confusion matrix computations and gets measures like accuracy 98.34%, sensitivity 98.34% Recall 97.34%, and F score 98.45%. These output results compete with present models and outperformance the methodology.

Název v anglickém jazyce

A deep learning approach for facial emotions recognition using principal component analysis and neural network techniques

Popis výsledku anglicky

In this work, advanced facial emotions are recognized using Neural network-based (NN) PCA methodology. The earlier models are cannot detect facial emotions with moving conditions but the CCTV and other advanced applications are mostly depending on moving object-based emotion recognition. The blurring, mask, and moving object-based facial image are applied to the training process, and at the testing condition, real-time facial images are applied. The PCA is extracting features and pre-processing the images with NN deep learning process. The proposed facial emotion recognition model is most useful for advanced applications. The design is finally verified through confusion matrix computations and gets measures like accuracy 98.34%, sensitivity 98.34% Recall 97.34%, and F score 98.45%. These output results compete with present models and outperformance the methodology.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

PHOTOGRAMMETRIC RECORD

ISSN

0031-868X

e-ISSN

1477-9730

Svazek periodika

37

Číslo periodika v rámci svazku

180

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

18

Strana od-do

435-452

Kód UT WoS článku

000888531500001

EID výsledku v databázi Scopus

2-s2.0-85143389375