Abstract
A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions
Highlights
Human facial expressions that people see visually are all around them
Since Facial Emotion Recognition (FER) has been catching wide attention in the researchers’ community, and less research with a 360-degree overview of this domain is found currently, the paper attempted to present all important aspects related to FER
The authors presented a brief review of methods and state-of-the-art models used in FER for different dataset categories
Summary
Human facial expressions that people see visually are all around them They are natural signals that help them understand emotions from any person in front of them or via images or videos. Human facial emotion recognition has been broadly used in numerous human-computer interactions such as smartphones, affective computing, intelligent control systems, psychological, behavioral study, pattern searching, defense, social sites, robotics, and other fields [3,4,5]. By evaluating these emotions, one could deliver maximum user satisfaction and feedback to improve current technologies. To create several Facial Emotion Recognition (FER) systems that have been evaluated for encoding and transmitting
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