An automatic improved facial expression recognition for masked faces.

Content Provider	Europe PMC
Author	ELsayed, Yasmeen ELSayed, Ashraf Abdou, Mohamed A.
Abstract	Automatic facial expression recognition (AFER), sometimes referred to as emotional recognition, is important for socializing. Automatic methods in the past two years faced challenges due to Covid-19 and the vital wearing of a mask. Machine learning techniques tremendously increase the amount of data processed and achieved good results in such AFER to detect emotions; however, those techniques are not designed for masked faces and thus achieved poor recognition. This paper introduces a hybrid convolutional neural network aided by a local binary pattern to extract features in an accurate way, especially for masked faces. The basic seven emotions classified into anger, happiness, sadness, surprise, contempt, disgust, and fear are to be recognized. The proposed method is applied on two datasets: the first represents CK and CK +, while the second represents M-LFW-FER. Obtained results show that emotion recognition with a face mask achieved an accuracy of 70.76% on three emotions. Results are compared to existing techniques and show significant improvement.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10067009&blobtype=pdf
ISSN	09410643
Journal	Neural Computing & Applications [Neural Comput Appl]
Volume Number	35
DOI	10.1007/s00521-023-08498-w
PubMed Central reference number	PMC10067009
Issue Number	20
PubMed reference number	37274419
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2023-04-01
Publisher Place	London
Access Restriction	Open
Rights License	Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2023
Subject Keyword	Convolution neural network Feature extraction Local binary pattern Facial expression recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software