Face Expression Recognition: A Survey on Hyperparameter Optimization

This review paper surveys a range of hyperparameter optimization techniques employed in the context of transformer models for facial expression recognition. Transformers have proven to be highly effective in various natural language processing and computer vision tasks. In the specific domain of facial expression recognition, they offer promise, but the appropriate tuning of hyperparameters is crucial for their optimal performance. Various techniques are available to train model parameters and various methods can be used to find the best hyperparameter values. The process of finding the best hyperparameter values, which enables the model to discover the best set of parameters to perform a given task, is referred to as hyperparameter optimization. The paper comprehensively explores and assesses various hyperparameter optimization methods to determine their impact on facial expression recognition model performance.