Facial expression recognition based on memristor convolutional neural network

Home > Archive>Volume 45, Issue 16, 2022 >93-101

Facial expression recognition based on memristor convolutional neural network
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                        Zhao YiboZhao Yibo
1.School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing 210044, China
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Jiang WenJiang Wen
1.School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing 210044, China
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Meng RuoyuMeng Ruoyu
1.School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing 210044, China
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Li YeningLi Yening
1.School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing 210044, China
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Affiliation:1.School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing 210044, China
Clc Number:TP391.41
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Abstract:

Memristors have the advantages of nanoscale size, low power consumption and similar to neural synapses, etc., and have broad application prospects in neural computing, image classification and other fields. In this paper, a facial expression recognition method based on memristor-based convolutional neural network is proposed. First, a memristor-based ResNet convolutional neural network is constructed and the ResNet network is pruned. Then the weights of all convolutional layers and fully connected layers of the ResNet model are mapped as the memductance values of memristors in the memristive crisscross array. The experimental results show that the recognition accuracy of the memristor-based convolutional neural network model on the FER2013 dataset is 63.82%, and the recognition accuracy on the CK+ dataset is 93.95%. Compared with the original convolutional network, the accuracy loss is only 0.31% and 0.76% respectively. Finally, the influence of the non-ideal characteristics of the memristor on the accuracy is tested, which provides a reference for the actual deployment of the memristor-based neural network.

Key words:memristor neural network; facial expression recognition; ResNet; convolutional neural network

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Online: April 07,2024
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