Facial expression recognition algorithm based on deep learning

Facial expression recognition (FER) plays a crucial role within the realm of computer vision. In the last several years, machine learning-powered FER methods have been fully developed. In contrast to conventional feature-based FER algorithms, FER algorithms based on machine learning have gained advantages. In this paper, we review the progress of FER algorithms driven by deep learning, and compare the horizontal effects of different network architectures based on CK+ data sets and VGG16 and other pre-trained deep learning models. At the same time, different data enhancement methods and learning rate adaptive adjustment methods are used to introduce attention mechanisms. A significant quantity of experimental data and model evaluation parameters show that the optimized facial expression recognition algorithm can improve the accuracy of facial expression recognition. Our method and optimization are effective, and good results are obtained on CK+ data set.