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International Journal of Neural Network, 2022, 3(4); doi: 10.38007/NN.2022.030408.

Deep Convolutional Neural Networks for Facial Expression Recognition


Yan Wang

Corresponding Author:
Yan Wang

Jinzhong University, Jinzhong, China


Facial expression recognition technology has been widely used in traffic, medical and criminal investigation and other fields. At present, facial expression recognition technology refers to feature extraction, classification and recognition through facial expression. In order to solve the shortcomings of the existing recognition technology research, this paper discusses the Softmax classifier function equation, facial expression and (DCNN), and briefly discusses the sample data and parameter configuration of the proposed facial expression recognition technology. Finally, the recognition process structure designed in this paper is applied to the five collected facial expressions (angry, sad, happy, afraid and calm) and the other two recognition models (GAN) and (LSTM) to compare the recognition rate. The experimental data show that the recognition rate of the proposed algorithm (CNN) is 95.2% and 94.9%, respectively, which is significantly better than that of the recognition model (GAN) and (LSTM). In the recognition of happiness, fear and calm, the average recognition rate of the proposed (CNN) reaches about 96.3%, while the highest recognition rate of the recognition model (GAN) and (LSTM) is only 92.7%. Therefore, it is verified that facial expression recognition based on depth (CNN) has a good performance effect.


Deep Learning, Convolutional Neural Network, Facial Expression, Expression Recognition

Cite This Paper

Yan Wang. Deep Convolutional Neural Networks for Facial Expression Recognition. International Journal of Neural Network (2022), Vol. 3, Issue 4: 61-68. https://doi.org/10.38007/NN.2022.030408.


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