Motivation

Detecting facial expression promotes communication between individuals. For humans, recognizing facial expressions and emotions is a basic skill that is learned at an early age. We can look at a person’s face and can quickly recognize the common emotions of anger, happiness, surprise, disgust, sadness, and fear. However, it is challenging to transfer this skill to a machine is a complex task. Despite of its complexity, automatic real-time detection on humans’ facial expressions can potentially be an useful computer vision technology in real life. Some possible practical applications are given below.

As these examples have revealed, real-time detection on facial expressions can be helpful in many real-life applications. Accuracy of detection becomes important in practical practices. Currently, deep learning neural networks appear to be a good tool in increasing accuracy of classification tasks. This meets our goal as we want to accurately classify a given image into a specific facial expression. Therefore, we are interested in finding a deep learning model that can give accurate results as well as can be easily applied to a real-time detection system.