Real Time Facial Emotion Recognition using Deep Learning and CNN

In this universe, a person’s mood may be read from their facial expressions. But one thing is for sure: every expression is a way that people communicate their emotions. Despite the fact that every person has a unique set of facial features and a range of emotions, artificial intelligence (AI)technology allows us to recognize these emotions. A face emotion-recognition AI is built using a variety of techniques, one of which is deep learning to build a convolutional neural network (CNN) to train the data set and obtain high prediction accuracy. Recognizing seven of the fundamental facial emotions out of hundreds is the major goal here. The most common facial emotions are those of happiness, sadness, neutrality, anger, surprise, fear, and disgust. There are plenty of ways to achieve this facial emotion recognition like using deep face algorithms, machine learning, by AI techniques, by Tensor flow etc. The accuracy of our model is between 70% to 80%. It is perhaps the simplest and efficient approach of all of them.