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22 June 2022 Facial expression recognition in classroom environment based on improved Xception model
Jia Tian, Jian Fang, Yue Wu
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Nowadays, there is a large amount of research on facial expression, and researchers have put forward a variety of effective methods. Now, due to the unsupervised learning function, deep learning is increasingly applied to facial expression recognition. The purpose of this paper is to study the recognition of facial expressions in a classroom environment based on an improved anomaly model. This paper proposes a new face recognition model, which is based on convolutional neural network. This paper introduces the construction and algorithm of the model and tests its performance for expression recognition through experiments. The experimental results show that the expression recognition accuracy of the facial expression recognition model proposed is 72.4%. The experiment compares this model with other models, the recognition accuracy of the proposed model architecture is 72.4%, and finds that this model has the best classification accuracy and the least parameters.

© 2022 SPIE and IS&T 1017-9909/2022/$28.00 © 2022 SPIE and IS&T
Jia Tian, Jian Fang, and Yue Wu "Facial expression recognition in classroom environment based on improved Xception model," Journal of Electronic Imaging 31(5), 051416 (22 June 2022).
Received: 26 February 2022; Accepted: 6 May 2022; Published: 22 June 2022

Facial recognition systems

Data modeling


Feature extraction

Evolutionary algorithms

Performance modeling

Image processing

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