Real-Time Emotion Recognition from Facial Expressions using Artificial Intelligence

Prashant Dhope, Mahesh B. Neelagar
{"title":"Real-Time Emotion Recognition from Facial Expressions using Artificial Intelligence","authors":"Prashant Dhope, Mahesh B. Neelagar","doi":"10.1109/AISP53593.2022.9760654","DOIUrl":null,"url":null,"abstract":"Emotion is the most important factor that distinguishes humans from robots. Machines are becoming more aware of human emotions as artificial intelligence advances. The objective of proposed method is to use artificial intelligence to build and construct a real-time facial emotion identification system. The proposed methodology has the capability of recognizing all the seven fundamental human face emotions. Those are angry, disgust, fear, happy, neutral, sad, and surprise. A self-prepared dataset is utilized to train the algorithm. The model is trained and facial expressions are recognized using a convolutional neural network. The real-time testing is accomplished using the Raspberry Pi 3B+ board and Pi-Camera. Using PyQt5, graphical user interface (GUI) is created for the system. The experimental result shows that, the proposed methodology has high recognition accuracy rate up to 99.88%.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":"11 1","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AISP53593.2022.9760654","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

Emotion is the most important factor that distinguishes humans from robots. Machines are becoming more aware of human emotions as artificial intelligence advances. The objective of proposed method is to use artificial intelligence to build and construct a real-time facial emotion identification system. The proposed methodology has the capability of recognizing all the seven fundamental human face emotions. Those are angry, disgust, fear, happy, neutral, sad, and surprise. A self-prepared dataset is utilized to train the algorithm. The model is trained and facial expressions are recognized using a convolutional neural network. The real-time testing is accomplished using the Raspberry Pi 3B+ board and Pi-Camera. Using PyQt5, graphical user interface (GUI) is created for the system. The experimental result shows that, the proposed methodology has high recognition accuracy rate up to 99.88%.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用人工智能从面部表情中实时识别情绪
情感是人类区别于机器人的最重要的因素。随着人工智能的进步,机器越来越能感知人类的情感。该方法的目的是利用人工智能技术构建实时面部情绪识别系统。所提出的方法具有识别所有七种基本人脸情绪的能力。它们是愤怒、厌恶、恐惧、快乐、中性、悲伤和惊讶。利用自己准备的数据集对算法进行训练。使用卷积神经网络对模型进行训练并识别面部表情。实时测试是使用树莓派3B+板和Pi- camera完成的。使用PyQt5,可以为系统创建图形用户界面(GUI)。实验结果表明,该方法的识别准确率高达99.88%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A 5.80 GHz Harmonic Suppression Antenna for Wireless Energy Transfer Application Crack identification from concrete structure images using deep transfer learning Energy Efficient VoD with Cache in TWDM PON ring Blockchain-based IoT Device Security A New Dynamic Method of Multiprocessor Scheduling using Modified Crow Search Optimization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1