Athlete facial micro-expression recognition method based on graph convolutional neural network

The recognition accuracy of athlete facial micro-expression is low due to insufficient consideration, failure to remove invalid data from the recognition data, and inaccurate extraction of micro-expression features. To this end, a new method for athlete facial micro-expression recognition based on image convolutional neural networks was studied. Firstly, the athlete's face data is preprocessed using facial alignment, unified frame, and optical flow extraction algorithms; Then, the graph convolutional neural network is used to extract athlete facial micro-expression features; Finally, to improve the performance of micro expression recognition tasks, a classification layer was added before the output layer of the network, and support vector machine algorithm was introduced to optimise and improve the graph convolutional neural network to adjust the discriminative boundaries between categories, achieving more accurate and effective micro expression recognition. The experimental results show that the proposed method can accurately extract micro-expression features, with a recognition accuracy of 97.0% and high convergence, effectively improving the recognition effect.