Machine and deep learning in facial expression recognition: a survey based on facial action units

Facial Expression Recognition (FER) has become increasingly relevant in engineering applications spanning security, healthcare, human-robot interaction, and intelligent systems. Accurate recognition of emotional states through facial cues is particularly important for enabling autonomous systems to interpret human intent in dynamic environments. This study presents a survey of recent advances in FER, with a focus on machine learning (ML) and deep learning (DL) methodologies developed between 2019 and 2024. Special attention is given to Facial Action Unit Detection (FAUD) as an intermediate and physiologically grounded representation for expression recognition. The analyzed literature is organized according to the final scope of the proposed models, distinguishing FAUD-oriented, FER-oriented, AU-assisted FER, and FAUD-to-FER approaches. Through this structured comparison, the survey identifies methodological trends, reported performance patterns, and the conditions under which FAUD-based intermediate modeling appears particularly advantageous for reliable FER.