Table tennis player expression recognition method based on Gabor multi directional feature fusion

In order to improve the recognition rate of table tennis players' expressions and reduce the recognition difference, this paper proposes an expression recognition method based on Gabor multi-directional feature fusion. After extracting multi-scale geometric features of facial expression image, the parameters of Gabor filter are optimised, multi-scale feature fusion and filtering are performed on the image, and block histogram features are extracted. The optimised multi-scale features are input into the generated confrontation network model to realise the recognition of table tennis players' expressions. Experimental results show that the maximum recognition rate of the method can reach 98.7%, and the minimum recognition difference is only 0.871. The feature results of the image in five scales and eight directions can be obtained, which shows that the method can accurately output the facial expression recognition results. Copyright © 2024 Inderscience Enterprises Ltd.