Facial emotions analysis with computer vision
Leverage deep neural networks to analyze and interpret facial expressions to recognize moods, emotions and assess specific indicators– such as eye gaze – to determine attentiveness. Machine learning models can be used to detect and categorize emotional states (happiness, sadness, anger, surprise, pain etc), as well as shifts in emotional states in response to particular events.
Facial emotions analysis can be conducted in real-time for a variety of purposes, including:
Healthcare
To monitor patient discomfort and responses to treatments.
Retail
To determine customers emotional response to marketing campaigns and merchandising.
Transport
To detect driver fatigue.
Education
To assess the level of student engagement with (predominantly online) course material.
*Disclaimer: Outcomes described and depicted are illustrative in nature. Learn more.