ABSTRACT
One of the most popular uses of machine learning in the worlds of websites, software, gaming, education, enterprises, and healthcare is the detection of human or animals’ emotions. It was once limited to evaluating or identifying human emotions. Numerous computer techniques, including Bayesian networks, Gaussian mixture models, and Hidden Markov Chains, have been developed to identify facial expressions both automatically and manually. Not only may emotions be deduced from muscular movements, but they can also be identified from written words, spoken words, gestures, and visual representations. This study aims to highlight potential novel behavioral techniques and research perspectives by examining and comparing the ways in which emotions are expressed and related in humans and other animals. The significance of animal emotion recognition, its applications, and the function that body language plays in determining an emotion's correctness with more precision are all covered by the author.
