The invention provides a human face expression recognition method based on a cost sensitive convolutional neural network. On the basis that the loss caused by wrong classification of the human face expression types is considered, the cost caused by wrong classification is fused in the whole process of model training and parameter optimization; in the model training process, different influences caused by wrong classification of different expression types are considered; and model parameters are adaptively adjusted according to the loss caused by wrong classification, so that the loss caused bywrong classification of the expression types is reduced to the maximum extent. The method solves the problem that the influences of the costs caused by wrong classification of different expression types are assumed to be equal or are equal by default in an existing human face expression recognition model based on a convolutional neural network, reduces the loss caused by wrong classification of expressions while improving the expression recognition precision, and has a relatively strong face emotion recognition capability.
Human face expression recognition method based on cost sensitive convolutional neural network