Live Facial Emotion Detection

Live Facial Emotion Detection

Soham Deshpande, Abhinandan Nahar, Achal Shah, Prachi Thete

Dept of Information Technology, MVPs KBT College of Engineering, Maharashtra, India

Abstract

Facial expression recognition is an important part of affective computing and artificial intelligence, and many academics have worked on it. However, because human face expressions change so subtly, most traditional techniques rely heavily on feature extraction for recognition accuracy. Meanwhile, deep learning has lately been a hot study issue in the field of machine learning, to simulate the organizational structure of the nerves in the human brain and combine low-level information to form a more abstract level. In this project, we use a deep convolutional neural network (CNN) to create a facial expression identification system that can uncover deeper feature representations of facial expressions and recognize them automatically. The suggested system is made up of three modules: input, recognition, and output Module. We introduce the expanded Facial Expression Detection Dataset, which can be used to simulate and assess recognition performance under various conditions (network structure, learning rate, and pre-processing).