FER-net: facial expression recognition using deep neural net

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017705" target="_blank" >RIV/62690094:18450/21:50017705 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s00521-020-05676-y" target="_blank" >https://link.springer.com/article/10.1007/s00521-020-05676-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-020-05676-y" target="_blank" >10.1007/s00521-020-05676-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

FER-net: facial expression recognition using deep neural net

Popis výsledku v původním jazyce

Automatic facial expression recognition (FER) is one of the most challenging tasks in computer vision. FER admits a wide range of applications in human–computer interaction, behavioral psychology, and human expression synthesis. Extensive works have been reported in this field, mainly, based on handcrafted features. However, it is a challenging task to accurately extract all the correlated handcrafted features due to the effect of variations caused by emotional state. Therefore, there is a quest for further research on accurately extracting relevant features that can capture changes in facial expressions (FEs) with high fidelity. In this study, we propose FER-net: a convolution neural network to distinguish FEs efficiently with the help of the softmax classifier. We implement our method FER-net along with twenty-one state-of-the-art methods and test them on five benchmarking datasets, namely FER2013, Japanese Female Facial Expressions, Extended CohnKanade, Karolinska Directed Emotional Faces, and Real-world Affective Faces. Seven FEs, namely neutral, anger, disgust, fear, happiness, sadness, and surprise, are considered in this work. The average accuracies on these datasets are 78.9%, 96.7%, 97.8%, 82.5% and 81.68%, respectively. The obtained results demonstrate that FER-net is preeminent in comparison with twenty-one state-of-the-art methods. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.

Název v anglickém jazyce

FER-net: facial expression recognition using deep neural net

Popis výsledku anglicky

Automatic facial expression recognition (FER) is one of the most challenging tasks in computer vision. FER admits a wide range of applications in human–computer interaction, behavioral psychology, and human expression synthesis. Extensive works have been reported in this field, mainly, based on handcrafted features. However, it is a challenging task to accurately extract all the correlated handcrafted features due to the effect of variations caused by emotional state. Therefore, there is a quest for further research on accurately extracting relevant features that can capture changes in facial expressions (FEs) with high fidelity. In this study, we propose FER-net: a convolution neural network to distinguish FEs efficiently with the help of the softmax classifier. We implement our method FER-net along with twenty-one state-of-the-art methods and test them on five benchmarking datasets, namely FER2013, Japanese Female Facial Expressions, Extended CohnKanade, Karolinska Directed Emotional Faces, and Real-world Affective Faces. Seven FEs, namely neutral, anger, disgust, fear, happiness, sadness, and surprise, are considered in this work. The average accuracies on these datasets are 78.9%, 96.7%, 97.8%, 82.5% and 81.68%, respectively. The obtained results demonstrate that FER-net is preeminent in comparison with twenty-one state-of-the-art methods. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature.

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

2021

Kód důvěrnosti údajů

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

Název periodika

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

—

Svazek periodika

33

Číslo periodika v rámci svazku

15

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

9125-9136

Kód UT WoS článku

000606448600003

EID výsledku v databázi Scopus

2-s2.0-85099288576