The ability for Artificial Intelligence to make decisions depend upon large datasets is largely changing the way organizations operate in today’s world. Each roadmap, each goal, and the decision have to be supported by a certain statistic or justification. As to why the organization is going in that direction. This is another reason why Data Engineers and Scientists are heavily paid throughout the world, As, they have the job to analyze and make predictions. Which can potentially put the company’s future at stake. 

Emotion AI

However, that’s not all that AI can do. Utilizing Computer Vision and Facial Recognition capabilities. Emotion AI can detect human emotions accurately. Along with that, considering you have a small dataset. It can identify people base on their facial features. Feelings such as anger, contempt, disgust. More can be easily identified and mapped by classifying each of these types as attributes.

Emotion AI Examples

An example of emotion AI detection is through Microsoft with the use of Cognitive Azure services. The Application Programming Interface (API) takes a sample image as input and classifies different parts of the face into different features. For instance, it classifies happiness on a scale of 0 to 1 & similarly. All other emotions are mapped. The result appears in JSON and developers can use it for their applications.

 

The applications for this simple result are massive. Imagine being able to detect emotions in any scenario by using a camera and a live feed being transmitting to a cloud server.

Sentiment analysis works in the same methods where you can analyze the reaction of your audience against the scenario they are currently in. This can be fairly accurate when the facial features are generally spontaneous and triggered by the feelings of humans. 

Sentiment Analysis Vs Emotion Analysis

Research papers are discussing the link between sentiment analysis and emotion analysis such as one which studies the reactions of patients. The study uses a state-of-the-art convolutional neural network and fed the face into an image classifier to recognize facial emotion. 

By classifying different features of a human face. Further, through the use of Computer Vision and Facial Recognition. Machines can identify what we are feeling in real-time. This helpful especially in robot-assisted operations to help ascertain if a particular procedure is not too painful for the patient.

Similarly, depression or anxiety can be identified through suitable algorithms implemented in a controlled environment. We can also document the reaction of candidates towards particular situations as well. 

Conclusion

If you are a new developer looking to specialize in a field. Facial Recognition and Machine Learning still have a lot to explore. The problem of optimization of these algorithms and the reduction of processing time remains a headache. or this could be something that you could take up as a research topic to work on in the future.