Research on expression recognition algorithm based on improved convolutional neural network

Xuejing Ding; Vladimir Y. Mariano

doi:10.1117/12.2680794

23 May 2023 Research on expression recognition algorithm based on improved convolutional neural network

Xuejing Ding, Vladimir Y. Mariano

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264537 (2023) https://doi.org/10.1117/12.2680794
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

The accuracy of traditional convolutional neural networks in expression recognition is relatively low, this paper makes improvements on the basis of ResNet network. On the one hand, the two 3*3 convolutional layers of ResNet residual block are replaced by two 1*1 convolutional layers plus a 3*3 convolutional layer, which not only maintains the accuracy of the network model but also reduces the amount of computation at the same time, the ReLU activation function is replaced with Mish. On the other hand, the convolutional attention mechanism is added to the network model to suppress the useless feature information, so as to improve the recognition accuracy of facial expressions. The experimental results show that the improved network model has significant improvement over the original network in RAF-DB and FERPlus data sets respectively.

Citation Download Citation

Xuejing Ding and Vladimir Y. Mariano "Research on expression recognition algorithm based on improved convolutional neural network", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264537 (23 May 2023); https://doi.org/10.1117/12.2680794

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