The week's pick
Reseach Article
Face Expressions Recognition by using Deep Learning
by
Salwa Almoshity,
Salema Younus,
Sarah Amer Al-asbaily
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 8 |
| Year of Publication: 2024 |
| Authors: Salwa Almoshity, Salema Younus, Sarah Amer Al-asbaily |
10.5120/ijca2024923432
|
Salwa Almoshity, Salema Younus, Sarah Amer Al-asbaily . Face Expressions Recognition by using Deep Learning. International Journal of Computer Applications. 186, 8 ( Feb 2024), 40-44. DOI=10.5120/ijca2024923432
@article{
10.5120/ijca2024923432,
author = {
Salwa Almoshity,
Salema Younus,
Sarah Amer Al-asbaily
},
title = { Face Expressions Recognition by using Deep Learning },
journal = {
International Journal of Computer Applications
},
issue_date = { Feb 2024 },
volume = { 186 },
number = { 8 },
month = { Feb },
year = { 2024 },
issn = { 0975-8887 },
pages = {
40-44
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume186/number8/face-expressions-recognition-by-using-deep-learning/
},
doi = { 10.5120/ijca2024923432 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-29T03:28:31.590897+05:30
%A Salwa Almoshity
%A Salema Younus
%A Sarah Amer Al-asbaily
%T Face Expressions Recognition by using Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 8
%P 40-44
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Facial expression recognition is a technology that uses biometric features to classify expressions in human faces. This technology plays a significant role in social communication since it conveys a lot of information about people, is considered a sentiment analysis tool, and is able to automatically recognize the seven basic or universal expressions: anger, contempt, disgust, fear, happiness, sadness, and surprise. Deep learning methods boost the learning process and facilitate the data creation task. In this work, the proposed approach used a non-classical technique, Inception-Resnet-v2, to pre-trained deep neural networks (DNNs) on more than a million images from the ImageNet and tested utilizing the face expression database from the Cohn-Kanade (CK+). The system had a loss validation of 0.014668% and attained 100% accuracy.
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Index Terms
Computer Science
Information Sciences
Keywords