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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Facial Expression Recognition via ResNet-18

verfasst von : Bin Li, Runda Li, Dimas Lima

Erschienen in: Multimedia Technology and Enhanced Learning

Verlag: Springer International Publishing

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Abstract

As an important part of human-computer interaction, facial expression recognition has become a hot research topic in the fields of computer vision, pattern recognition, artificial intelligence, etc., and plays an important role in our daily life. With the development of deep learning and convolutional neural network, the research of facial expression recognition has also made great progress. Moreover, in the current face emotion recognition research, there are problems such as poor generalization ability of network model. The extraction of traditional facial expression recognition features is complex and the effect is not ideal. In order to improve the effect of facial expression recognition, we propose a feature extraction method for deep residual network, and use deep residual network ResNet-18 to extract the features of the data set. Through the experimental simulation of the specified data set, it can be proved that this model is superior to state-of-the-art methods model.

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Metadaten
Titel
Facial Expression Recognition via ResNet-18
verfasst von
Bin Li
Runda Li
Dimas Lima
Copyright-Jahr
2021
Buch
Multimedia Technology and Enhanced Learning

Print ISBN: 978-3-030-82564-5

Electronic ISBN: 978-3-030-82565-2

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-82565-2_24