Emotion Detection Using Adaboost and CNN

Abstract

Recent advances in Artificial Intelligence techniques are largely to blame for the rise in interest in the recognition of facial expressions. The process of distinguishing different human emotions using facial expressions is known as emotion detection. The system picked up on emotions like sadness, happiness, rage, fear, surprise, neutrality, and contempt. This paper is focussed on detecting 7 emotions through Haar-cascade, Adaboost and Convolutional Neural Networks algorithms. Compared to other existing systems, CNN's emotion recognition improved accuracy while reducing space complexity. Lie detectors, robotics, and artistic expression are some more uses of this system.