Emotion Recognition using AutoEncoders and Convolutional Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F18%3A63520522" target="_blank" >RIV/70883521:28140/18:63520522 - isvavai.cz</a>

Výsledek na webu

<a href="https://mendel-journal.org/index.php/mendel/article/view/31" target="_blank" >https://mendel-journal.org/index.php/mendel/article/view/31</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.13164/mendel.2018.1.113" target="_blank" >10.13164/mendel.2018.1.113</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Emotion Recognition using AutoEncoders and Convolutional Neural Networks

Popis výsledku v původním jazyce

Emotions demonstrate people&apos;s reactions to certain stimuli. Facial expression analysis is often used to identify the emotion expressed. Machine learning algorithms combined with artificial intelligence techniques have been developed in order to detect expressions found in multimedia elements, including videos and pictures. Advanced methods to achieve this include the usage of Deep Learning algorithms. The aim of this paper is to analyze the performance of a Convolutional Neural Network which uses AutoEncoder Units for emotion-recognition in human faces. The combination of two Deep Learning techniques boosts the performance of the classification system. 8000 facial expressions from the Radboud Faces Database were used during this research for both training and testing. The outcome showed that five of the eight analyzed emotions presented higher accuracy rates, higher than 90%.

Název v anglickém jazyce

Emotion Recognition using AutoEncoders and Convolutional Neural Networks

Popis výsledku anglicky

Emotions demonstrate people&apos;s reactions to certain stimuli. Facial expression analysis is often used to identify the emotion expressed. Machine learning algorithms combined with artificial intelligence techniques have been developed in order to detect expressions found in multimedia elements, including videos and pictures. Advanced methods to achieve this include the usage of Deep Learning algorithms. The aim of this paper is to analyze the performance of a Convolutional Neural Network which uses AutoEncoder Units for emotion-recognition in human faces. The combination of two Deep Learning techniques boosts the performance of the classification system. 8000 facial expressions from the Radboud Faces Database were used during this research for both training and testing. The outcome showed that five of the eight analyzed emotions presented higher accuracy rates, higher than 90%.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

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

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<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Mendel

ISSN

1803-3814

e-ISSN

—

Svazek periodika

24

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

8

Strana od-do

113-120

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85072024940