Emotion Recognition Using Convolutional Neural Networks
Abstract
Emotion has an important role in daily life, as it helps people better communicate with and understand each other more efficiently. Facial expressions can be classified into 7 categories: angry, disgust, fear, happy, neutral, sad and surprise. How to detect and recognize these seven emotions has become a popular topic in the past decade. In this paper, we develop an emotion recognition system that can apply emotion recognition on both still images and real-time videos by using deep learning. We build our own emotion recognition classification and regression system from scratch, which includes dataset collection, data preprocessing , model training and testing. Given a certain image or a real-time video, our system is able to show the classification and regression results for all of the 7 emotions. The proposed system is tested on 2 different datasets, and achieved an accuracy of over 80\%. Moreover, the result obtained from real-time testing proves the feasibility of implementing convolutional neural networks in real time to detect emotions accurately and efficiently.
Cite
@article{arxiv.2504.03010,
title = {Emotion Recognition Using Convolutional Neural Networks},
author = {Shaoyuan Xu and Yang Cheng and Qian Lin and Jan P. Allebach},
journal= {arXiv preprint arXiv:2504.03010},
year = {2025}
}