FER-net: facial expression recognition using deep neural net
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
FER-net: facial expression recognition using deep neural net
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computing and Applications
ISSN
0941-0643
e-ISSN
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Volume of the periodical
33
Issue of the periodical within the volume
15
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
9125-9136
UT code for WoS article
000606448600003
EID of the result in the Scopus database
2-s2.0-85099288576