  Facial expression recognition (FER) is a challenging topic in artificial
intelligence. Recently, many researchers have attempted to introduce Vision
Transformer (ViT) to the FER task. However, ViT cannot fully utilize emotional
features extracted from raw images and requires a lot of computing resources.
To overcome these problems, we propose a quaternion orthogonal transformer
(QOT) for FER. Firstly, to reduce redundancy among features extracted from
pre-trained ResNet-50, we use the orthogonal loss to decompose and compact
these features into three sets of orthogonal sub-features. Secondly, three
orthogonal sub-features are integrated into a quaternion matrix, which
maintains the correlations between different orthogonal components. Finally, we
develop a quaternion vision transformer (Q-ViT) for feature classification. The
Q-ViT adopts quaternion operations instead of the original operations in ViT,
which improves the final accuracies with fewer parameters. Experimental results
on three in-the-wild FER datasets show that the proposed QOT outperforms
several state-of-the-art models and reduces the computations.
