Abstract
A person’s face can reveal their mood, and microexpressions, although brief and involuntary, are also authentic. People can recognize facial gestures; however, their accuracy is inconsistent, highlighting the importance of objective computational models. Various artificial intelligence models have classified microexpressions into three categories: positive, negative, and surprise. However, it is still significant to address the basic Ekman microexpressions (joy, sadness, fear, disgust, anger, and surprise). This study proposes a Transformers-based machine learning model, trained on CASME, SAMM, SMIC, and its own datasets. The model offers comparable results with other studies when working with seven classes. It applies various component-based techniques ranging from ViT to optical flow with a different perspective, with low training rates and competitive metrics comparable with other publications on a laptop. These results can serve as a basis for future research.