An extensive study of facial expression recognition using artificial intelligence techniques with different datasets

Machine and deep learning (DL) algorithms have advanced to a point where a wide range of crucial real-world computer vision problems can be solved. Facial Expression Recognition (FER) is one of these applications; it is the foremost non-verbal intentions and a fascinating study of symmetry. A prevalent application of deep learning has become the area of vision, where facial expression recognition has emerged as one of the most promising new frontiers. Latterly deep learning-based FER models have been plagued by technical problems, including under-fitting and over-fitting. Probably inadequate information is used for training and expressing ideas. With these considerations in mind, this article gives a systematic and complete survey of the most cutting-edge AI strategies and gives a conclusion to address the aforementioned problems. It is also a scheme of classification for existing facial proposals in compact. This survey analyses the structure of the usual FER method and discusses the feasible technologies that may be used in its respective elements. In addition, this study provides a summary of seventeen widely-used FER datasets that reviews functioning novel machine and DL networks suggested by academics and outline their benefits and liability in the context of facial expression acknowledgment based on static replicas. Finally, this study discusses the research obstacles and open consequences of that well-conditioned face expression recognition scheme.