Abstract: Aiming at the problem that it is difficult to extract and recognize the dynamic features of facial expression due to local occlusion, a dynamic recognition algorithm of facial expression with local occlusion based on deep learning is proposed, a deep belief network model is established, taking the output value of the previous layer as the input value of the next layer, a feature stacking unit is designed, the distribution of state variables of neurons in the visible layer, and the state variables of hidden neurons are calculated by taking the state value of the visible layer as the input value of the hidden layer according to the dynamic correlation of facial features. The recognition process is divided into two steps: training and forward propagation. The feature change rule is output. In the forward propagation process, the pixel point that conforms to the rule change is found, and the weight of the pixel point is solved. And as a loss function standard, the recognition weight of multiple positions on the face is used to constrain the recognition rate, and the dynamic recognition of facial partial occlusion expression is completed. Experimental data show that the proposed method can reduce image distortion and detail loss, improve image resolution, and achieve high recognition rate. It can complete efficient recognition for different local occlusion situations.

Key words: deep learning, dynamic facial expression recognition, dynamic relevance, deep belief network model, hide layer