Aiming at the problems of inaccurate facial expression recognition and large amount of calculation under multi-perspective in real life

a facial expression recognition model MVResNet-FER is proposed

which is based on multi-perspective feature fusion under deep residual convolution.The residual block in ResNet is first improved and the conventional convolutional network is replaced with a depthwise separable network.Second

a CBAM module is added to enhance the extraction of effective features under multi-perspective and the supplementation of shallow feature information.Then use the RReLu activation function to replace the original ReLu to avoid deactivation of some nodes when the gradient is large.Finally

the global average pooling layer is used instead of the fully connected layer to achieve dimensionality reduction

and the generated feature vector is sent to Softmax for classification Experiments show that the proposed method produces excellent results on the CK+ and RaFD datasets

which can effectively improve the accuracy of facial expression recognition.