{"title":"利用人工智能从面部表情中实时识别情绪","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":"{\"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}","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}
Real-Time Emotion Recognition from Facial Expressions using Artificial Intelligence
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%.