<![CDATA[Numerous domains, including safety, health, and human-machine interfaces, have garnered significant attention from researchers. Within this field, there is a notable interest in developing methodologies for interpreting and encoding facial expressions, as well as extracting pertinent features for more accurate computer-based predictions. Leveraging the remarkable advancements in deep learning, various architectural approaches are explored to enhance performance outcomes. The primary objective of this paper is to conduct an examination of recent research endeavors pertaining to automatic facial emotion recognition (FER) through the utilization of deep learning techniques. We emphasize the treatment of these contributions, elucidate the architectural frameworks employed, and outline the databases that have been utilized. Additionally, we present a comprehensive assessment of the progress achieved by comparing the methodologies proposed and the corresponding results obtained. This paper aims to provide valuable insights and guidance to researchers in this field by reviewing recent developments and suggesting avenues for further enhancements]]>
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