Face Expression Recognition With Vision Transformer and Local Mutual Information Maximization

With the development of society, the proportion of the elderly population is increasing year by year, and population aging has become a global trend. As people grow older, the shape and texture features of the face will change to some extent, which increases the difficulty of face recognition. The key point to getting good performance in the elderly expression recognition task is how to capture the facial features effectively. In this paper, we use a Vision Transformer network and apply the Local Mutual Information Maximization (LMIM) technique to it. The correlation between the input map and the high-level hidden representation is evaluated through the optimization of local mutual information. This also allows for the identification of the most pertinent features between the input image and the hidden representation. Finally, we evaluated this method on three datasets, EFED(Self-constructed dataset on the elderly), FER2013, and Seeprettyface as benchmarks, and the experiments show that the experimental results after the addition of local mutual information maximization (LMIM) are improved by about 4% to 9% over the original.