Emotion AI deals with the recognition, interpretation, process, and simulation of human feelings and emotions. A human affective state can be recognized through computer vision-based facial expression recognition or by analyzing autonomic nervous system activity collected through psychophysiological signals of biosensors (e.g., EEG, PPG, EMG). There are many success stories of businesses leveraging emotion AI in marketing, customer service, healthcare, education, and gaming. The new Emotion AI research direction could be the role of emotions in effective negotiations, the harmful effects of anxiety on performance, and how advertisers can effectively capture and keep viewers' attention by evoking certain emotional responses.
The topic is relevant to computer science, computer engineering, and data science students who are enthusiasts in machine learning, deep learning, image processing, digital signal processing, and business intelligence.
The global emotion detection and recognition market was valued at $18.8 billion in 2020 and is projected to reach $103.1 billion by 2030, growing at a CAGR of 18.7% from 2021 to 2030.
After completion of this research, students will learn affective computing methods for business intelligence: Emotion Detection and Recognition Market by Software Tool (Facial Expression and Emotion Recognition, Gesture and Posture Recognition, Voice Recognition), by Application (Law Enforcement Surveillance and Monitoring, Entertainment and Consumer Electronics, Marketing and Advertising, Others), by Technology (Pattern Recognition Network, Machine Learning, Natural Language Processing, Others), by End User (Commercial, Entertainment, Retail, Others).
