Static facial expression emotion recognition method using spatiotemporal graph convolution

In order to improve the Matthews correlation coefficient (MCC) and consistency index of facial expression features in emotion recognition, a static facial expression emotion recognition method using spatiotemporal graph convolution is proposed. Firstly, by standardising facial images through eye localisation, correcting tilted expressions through rotation, estimating pixel values through bilinear interpolation, and combining histogram equalisation techniques, greyscale processing of static facial expression images has been achieved. Secondly, by applying two-dimensional Gabor wavelet filtering to static facial images, the time-frequency localisation characteristics of Gabor wavelets are utilised to accurately extract texture details of different frequencies and directions in facial images. Finally, the spatiotemporal graph convolution method is used to extract spatial features and achieve effective recognition of static facial expressions and emotions. In the static facial expression emotion recognition experiment, the Matthews correlation coefficient remained above 0.9, and the consistency index of expression features remained above 0.91.