Facial Expression Recognition Based on Multi-scale Feature Fusion Convolutional Neural Network and Attention Mechanism

Abstract

Facial expression recognition plays an important role in human-computer interaction. At present, the traditional algorithm applied to facial expression recognition is not accurate enough, and deep learning methods have problems such as a large number of network model parameters, insufficient generalization ability, and high requirements on hardware devices in actual deployment. To solve these problems, a lightweight facial expression recognition model based on multi-scale feature fusion and attention mechanism is proposed in this paper. Firstly, a multi-scale deeply separable densely connected convolutional neural network is constructed, which can greatly reduce the number of parameters and computation. The convolution kernel of different scales can obtain the receptive fields of different scales, and finally integrate the features of multiple scales, which is beneficial to the learning of the features of different scales. A dense connection reduces gradient disappearance and improves model generalization performance. Secondly, group convolution is introduced to further reduce the number of parameters. Finally, the attention mechanism and multi-scale were integrated to improve the classification accuracy of the model. The network model proposed in this paper has been trained and verified on RAF-DB, FER2013, CK+ and FERPlus datasets. Experimental results show that compared with other classical convolutional neural networks, the model proposed in this paper can significantly reduce the number of parameters and calculation amount while ensuring high accuracy. In the future, it is of great significance for the practical application of facial expression recognition in medical monitoring, education assistance, traffic warning and other fields.