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Detection of Facial Expression
Abstract
Human emotions are reflected in facial expressions. The focus of attention, intention, motivation, and emotion are just a few of the social cues it provides to the viewer. It is thought to be an effective method for communicating in silence. These expressions can be analyzed to provide a much deeper understanding of human behavior. In recent years, AIbased facial expression recognition (FER) has emerged as one of the most important areas of research with various applications in dynamic analysis, pattern recognition, interpersonal interaction, mental health monitoring, and a variety of other fields. However, a new FER analysis framework for the growing amount of visual data generated by videos and photographs has been urgently required due to the global push toward online platforms by the Covid-19 pandemic. Additionally, the FER study must take into account the various emotions-related facial expressions of children, adults, and seniors. There has been a lot of research done in this area. However, it lacks a comprehensive literature review that identifies aligned future directions and highlights previous accomplishments. In this paper, the authors provide an in-depth analysis of AI-based FER techniques, focusing on datasets, feature extraction techniques, algorithms, and the most recent developments in facial expression identification applications. This is, to the best of the author's knowledge, the only review paper that covers all aspects of FER across age groups and will have a significant impact on the research community in the years to come.
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