Comprehensive Review and Analysis on Facial Emotion Recognition: Performance Insights into Deep and Traditional Learning with Current Updates and Challenges

In computer vision and artificial intelligence, automatic facial expression-based emotion identification of humans has become a popular research and industry problem. Recent demonstrations and applications in several fields, including computer games, smart homes, expression analysis, gesture recognition, surveillance films, depression therapy, patient monitoring, anxiety, and others, have brought attention to its significant academic and commercial importance. This study emphasizes research that has only employed facial images for face expression recognition (FER), because facial expressions are a basic way that people communicate meaning to each other. The immense achievement of deep learning has resulted in a growing use of its much architecture to enhance efficiency. This review is on machine learning, deep learning, and hybrid methods’ use of preprocessing, augmentation techniques, and feature extraction for temporal properties of successive frames of data. The following section gives a brief summary of assessment criteria that are accessible to the public and then compares them with benchmark results the most trustworthy way to assess FER-related research topics statistically. In this review, a brief synopsis of the subject matter may be beneficial for novices in the field of FER as well as seasoned scholars seeking fruitful avenues for further investigation. The information conveys fundamental knowledge and provides a comprehensive understanding of the most recent state-of-the-art research.