Facial Expression Recognition Based on Improved Residual Block Network

Facial Expression Recognition Based on Improved Residual Block Network

Facial expression recognition is widely used, but there are some problems such as complex scenes, lack of data sets and low recognition rate. In this paper, we construct a new network model and name it RNFC. The RNFC network adopts 6 improved residual blocks to extract features. Features are passed into the fully connected layer by flattening the data, and Dropout techniques are introduced between the fully connected layers to prevent overfitting of the model. Based on the pytorch framework, we use a cross-entropy loss function to improve the training speed of the network. And perform denoising and enhancement pre-processing on the FER2013 dataset. The RNFC network is trained and tested on the pretreated FER2013. It has a higher recognition rate than classical networks such as VGGnet19 and ResNet18.