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Deep learning-based facial emotion recognition for human–computer interaction applications

Deep learning-based facial emotion recognition for human–computer interaction applications One of the most significant fields in the man–machine interface is emotion recognition using facial expressions. Some of the challenges in the emotion recognition area are facial accessories, non-uniform illuminations, pose variations, etc. Emotion detection using conventional approaches having the drawback of mutual optimization of feature extraction and classification. To overcome this problem, researchers are showing more attention toward deep learning techniques. Nowadays, deep-learning approaches are playing a major role in classification tasks. This paper deals with emotion recognition by using transfer learning approaches. In this work pre-trained networks of Resnet50, vgg19, Inception V3, and Mobile Net are used. The fully connected layers of the pre-trained ConvNets are eliminated, and we add our fully connected layers that are suitable for the number of instructions in our task. Finally, the newly added layers are only trainable to update the weights. The experiment was conducted by using the CK + database and achieved an average accuracy of 96% for emotion detection problems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Deep learning-based facial emotion recognition for human–computer interaction applications

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References (19)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021
ISSN
0941-0643
eISSN
1433-3058
DOI
10.1007/s00521-021-06012-8
Publisher site
See Article on Publisher Site

Abstract

One of the most significant fields in the man–machine interface is emotion recognition using facial expressions. Some of the challenges in the emotion recognition area are facial accessories, non-uniform illuminations, pose variations, etc. Emotion detection using conventional approaches having the drawback of mutual optimization of feature extraction and classification. To overcome this problem, researchers are showing more attention toward deep learning techniques. Nowadays, deep-learning approaches are playing a major role in classification tasks. This paper deals with emotion recognition by using transfer learning approaches. In this work pre-trained networks of Resnet50, vgg19, Inception V3, and Mobile Net are used. The fully connected layers of the pre-trained ConvNets are eliminated, and we add our fully connected layers that are suitable for the number of instructions in our task. Finally, the newly added layers are only trainable to update the weights. The experiment was conducted by using the CK + database and achieved an average accuracy of 96% for emotion detection problems.

Journal

Neural Computing and Applications – Springer Journals

Published: Nov 1, 2023

Keywords: Human–computer interaction; Transfer learning; Resnet50; VGG 19; Inception V3; MobileNet

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