Facial Expression Recognition Based on Improved VGG16 Convolutional Neural Network
Pages 162 - 168
Abstract
Facial expression recognition has the characteristics of complexity and variability, which makes it a very popular research topic at present. Based on the VGG16 convolutional neural network, a new deep learning expression recognition method is proposed, which can obviously improve the disadvantage of low accuracy of traditional expression recognition methods. The new network is based on the basic structure of the VGG16 network, meanwhile uses a single graphics processing unit for training. Firstly, the VGG16 network is divided into 5 Blocks, and then the last 3 Blocks are fused with features, and the Spatial Group Enhance (SEG) attention module is added. Finally, the redundant fully connected layers were deleted, and the final classification result was output by one fully connected layer, which effectively reduced the parameters of the neural network. Experimental results on FER2013 dataset and CK+ dataset show that the recognition rate of the new network for facial expression reaches 68.85%and 97.46%respectively, which is higher than that of other traditional facial expression recognition methods.
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Index Terms
- Facial Expression Recognition Based on Improved VGG16 Convolutional Neural Network
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Published In
December 2023
435 pages
ISBN:9798400716430
DOI:10.1145/3654446
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 03 May 2024
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SPCNC 2023
SPCNC 2023: The 2nd International Conference on Signal Processing, Computer Networks and Communications
December 8 - 10, 2023
Xiamen, China
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