Emotion Recognition Using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2504.03010v1
• Date: Thu, 03 Apr 2025 20:08:32 GMT
• Title: Emotion Recognition Using Convolutional Neural Networks
• Authors: Shaoyuan Xu, Yang Cheng, Qian Lin, Jan P. Allebach,
• Abstract summary: We develop an emotion recognition system that can apply emotion recognition on both still images and real-time videos by using deep learning.<n>The proposed system is tested on 2 different datasets, and achieved an accuracy of over 80%.
• Score: 11.243571725357823
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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