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<p>This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately. Firstly, we introduce the convolutional neural network, which consists of a convolutional layer, pooling layer and fully connected layer in the deep learning neural network, and propose the variational self-encoder face emotion recognition algorithm based on the convolutional neural network in combination with the facial emotion recognition process. The correct recognition rates achieved by EVA using 32×32 pixels, LBP features and 50-dimensional Gabor wavelet features are 95.13%, 96.74% and 98.84%, respectively. In terms of emotion recognition ability, the recognition accuracy percentages were around 70% for large emotion fluctuations and around 30% for small emotion fluctuations. Since the neutral facial emotion is not well distinguished, the recognition degree of neutral facial emotion is only 80%, and the recognition degree using the self-encoder-based face emotion recognition algorithm in several facial features extraction is above 80%, and the recognition degree with neutral emotion removed is up to about 90%. Therefore, the algorithm in this paper has good classification performance and can recognize and analyze facial emotion features accurately.</p>
</abstract>

This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately. Firstly, we introduce the convolutional neural network, which consists of a convolutional layer, pooling layer and fully connected layer in the deep learning neural network, and propose the variational self-encoder face emotion recognition algorithm based on the convolutional neural network in combination with the facial emotion recognition process. The correct recognition rates achieved by EVA using 32×32 pixels, LBP features and 50-dimensional Gabor wavelet features are 95.13%, 96.74% and 98.84%, respectively. In terms of emotion recognition ability, the recognition accuracy percentages were around 70% for large emotion fluctuations and around 30% for small emotion fluctuations. Since the neutral facial emotion is not well distinguished, the recognition degree of neutral facial emotion is only 80%, and the recognition degree using the self-encoder-based face emotion recognition algorithm in several facial features extraction is above 80%, and the recognition degree with neutral emotion removed is up to about 90%. Therefore, the algorithm in this paper has good classification performance and can recognize and analyze facial emotion features accurately.

Research on face emotion recognition algorithm based on deep learning neural network

Anhui Wenda University of Information Engineering

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately....

Research on face emotion recognition algorithm based on deep learning neural network

Data publikacji: 07 paź 2023

Zakres stron: -

Otrzymano: 11 gru 2022

Przyjęty: 17 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00533

Słowa kluczowe
Face emotion recognition, Deep learning, Convolutional neural network, Variational self-encoder, Cohn-Kanade emotion database

© 2023 Yan Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on face emotion recognition algorithm based on deep learning neural network

Data publikacji: 07 paź 2023

Zakres stron: -

Otrzymano: 11 gru 2022

Przyjęty: 17 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00533

Słowa kluczoweFace emotion recognition, Deep learning, Convolutional neural network, Variational self-encoder, Cohn-Kanade emotion database

© 2023 Yan Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Face emotion recognition, Deep learning, Convolutional neural network, Variational self-encoder, Cohn-Kanade emotion database