Emotion Recognition

This application analyzes facial expressions and determines emotions in an image or video file. It can also detect emotions in a live video feed (such as a web camera, for example). The application can also identify age, gender, and ethnicity of the individuals in images/videos.



Content creators frequently seek feedback on their content from viewers. Today, the process is usually to hire a market research agency to gather, compile, and return feedback.

This approach suffers from two major drawbacks:

  1. The process is manual and slow.
  2. The feedback is not necessarily objective as people tend to give favourable feedback regardless of how they really felt.

Content creators often find the real world results incongruent with the results obtained from the feedback.


One way to get real feedback is to identify emotions in real time as users watch content on their devices. By identifying emotions and superimposing the results on the user's gaze, it is possible to compute the user's level of engagement and also identify the parts of the content that are the most interesting.

This approach gives content creators a deep level of understanding and provides a window into feedback honesty


Use cases

Market Research

Adaptive Learning

Customer Journey

Ambient Intelligence