Simulation and implementation of deep-learning-based expression recognition

Facial expressions are a challenging area in image recognition, and ensuring the universality of the model in the presence of a large sample size is a difficult point in such research. This article conducts repeatability experiments on two extreme scenarios: high-quality small samples and low-quality large samples, to explore solutions. Through transfer learning, data augmentation, and substantive evaluation of confusion matrices, the article demonstrates the accurate classification of seven facial expressions and the design of a 4-class model with high universality, which is presented in the form of an APP.