Facial Expression Recognition Method Based on Octonion Orthogonal Feature Extraction and Octonion Vision Transformer

Abstract

In the field of artificial intelligence, facial expression recognition (FER) in natural scenes is a challenging topic. In recent years, vision transformer (ViT) models have been applied to FER tasks. The direct use of the original ViT structure consumes a lot of computational resources and longer training time. To overcome these problems, we propose a FER method based on octonion orthogonal feature extraction and octonion ViT. First, to reduce feature redundancy, we propose an orthogonal feature decomposition method to map the extracted features onto seven orthogonal sub-features. Then, an octonion orthogonal representation method is introduced to correlate the orthogonal features, maintain the intrinsic dependencies between different orthogonal features, and enhance the model’s ability to extract features. Finally, an octonion ViT is presented, which reduces the number of parameters to one-eighth of ViT while improving the accuracy of FER. Experimental results on three commonly used facial expression datasets show that the proposed method outperforms several state-of-the-art models with a significant reduction in the number of parameters.