A Machine Learning Emotion Detection Platform to Support Affective Well Being

This paper describes a new emotional detection system based on a video feed in real-time. It demonstrates how a bespoke machine learning support vector machine (SVM) can be utilized to provide quick and reliable classification. Features used in the study are 68-point facial landmarks. In a lab setting, the application has been trained to detect six different emotions by monitoring changes in facial expressions. Its utility as a basis for evaluating the emotional condition of people in situations using video and machine learning is discussed.

I. Introduction

Emotions are an integral part of experiencing the world. Functioning emotions help us to perceive, think, and act correctly. The crucial role of emotions in general well-being becomes self-evident when they become dysfunctional. Consider the fact that one of the main aims of psychotherapy is to help people deal with difficult emotions [1]; that the likelihood of experiencing psychopathology has been linked to the tendency to experience extreme levels of emotions [2]; that our ability to make seemingly innocuous and everyday choices, such as what clothes to wear, becomes impaired if the areas related to emotions in the brain are damaged [3]. This latter example of dysfunction is of particular concern as people increasingly live longer and as a result become more susceptible to neurodegenerative diseases, such as dementia.