Abstract: Aiming at the problem of poor pose and lighting robustness in complex environments, we proposed a facial expression recognition method that integrated an extended local binary pattern and multi-scale network structure. The method expanded the receptive field of the traditional local binary pattern and enhanced spatial correlations between pixels, reducing noise interference of lighting on facial expression recognition. By evenly dividing  the feature maps  into several subsets in the channel dimension, and multi-scale features of the feature map were extracted by using different  numbers of identical convolutional blocks, it effectively processed facial  pose variations. Experimental results on the Fer2013 and RAF-DB datasets show  that the proposed method can effectively improve  the accuracy and robustness of facial expression recognition, offering an effective solution for  facial expression recognition in complex environments.

Key words: facial expression recognition, local binary pattern, multi-scale network, convolutional neural network