The invention provides a facial expression recognition method based on an improved capsule network. The facial expression recognition method comprises the following steps of: inputting sample picturesinto the improved capsule network for training; and inputting an on-site live-action picture into the improved capsule network for recognition, and extracting a facial expression in the live-action picture; the step of inputting the sample pictures into the improved capsule network for training specifically comprises the following steps of: S1, extracting face regions from the pictures through amulti-task convolutional neural network; S2, performing marking processing on the extracted face regions to obtain expressions and the head postures of the face regions; S3, inputting the expressionsand the head postures of the face regions into a generative adversarial network, and generating the face regions with the expressions for the generative adversarial network; and S4, inputting the faceregions with the expressions into the improved capsule network to train the improved capsule network. Therefore, the expressions of the regions under different postures can be accurately recognized without considering the posture conditions of human bodies, the recognition efficiency can be effectively improved while the recognition accuracy can be ensured.
Facial expression recognition method based on improved capsule network