Publication details
Emotion recognition using machine learning have plenty of applications in areas such as
education, healthcare, marketing, and entertainment, and have the potential to improve
human-computer interaction and communication. However, appropriate recognition of
emotions remains a challenging task due to the complexity and inconstancy of human
behavior, and ongoing research is needed to achieve the performance and robustness of
machine learning models. Emotion recognition entangles the use of algorithms and
statistical models to automatically acknowledge the patterns and features in visual and
auditory data that fit specific emotional states and nonverbal behaviors. The process
typically meets several steps, including data collection, feature extraction, model training,
and model testing.