Abstract: Facial emotion recognition (FER) has become an important topic in the fields of computer vision and artificial intelligence due to its great academic and commercial potential. Although FER can be performed using multiple sensors, this review focuses on studies using facial images exclusively, since visual expressions areone of the main channels of information in human communication. Automatic emotion recognition based onfacial expressions is an interesting research area that has been applied and applied in various fields such as safety, health and human-computer interface. Researchersin this field are interested in developing techniquesto interpret, encode facial expressions and extract these features for better prediction by computer. With the remarkable success of deep learning, different types of architectures of this technique are exploited to achieve better performance. The purpose of this paperisto conduct a study ofrecent work on automatic facial emotionrecognition (FER) via deep learning. We highlight these contributions, the architectures and the databases used, and we show the progress achieved by comparing the proposed methods and the obtained results. The purpose of this paper is to serve and guide researchers by reviewing recent work and providing insights to improve the field.