{"title":"使用面部情绪和眼睛宽高比检测睡意","authors":"Sunsern Ceamanunkul, Sanchit Chawla","doi":"10.1109/ICSEC51790.2020.9375240","DOIUrl":null,"url":null,"abstract":"Drowsy drivers are a major cause of many road accidents around the world. Facial emotions are known to be one of the visual cues for detecting drowsiness. In this paper, we propose a machine learning approach to drowsiness detection based on using a combination of facial emotion features extracted by using deep convolutional neural networks (CNN) and eye-aspect-ratio (EAR) features. The combined feature vectors are then used for training a classifier. From our experiments, we obtain a classification accuracy of 81.7% when we use the combined features with a support vector machines (SVM) classifier.","PeriodicalId":158728,"journal":{"name":"2020 24th International Computer Science and Engineering Conference (ICSEC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Drowsiness Detection using Facial Emotions and Eye Aspect Ratios\",\"authors\":\"Sunsern Ceamanunkul, Sanchit Chawla\",\"doi\":\"10.1109/ICSEC51790.2020.9375240\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Drowsy drivers are a major cause of many road accidents around the world. Facial emotions are known to be one of the visual cues for detecting drowsiness. In this paper, we propose a machine learning approach to drowsiness detection based on using a combination of facial emotion features extracted by using deep convolutional neural networks (CNN) and eye-aspect-ratio (EAR) features. The combined feature vectors are then used for training a classifier. From our experiments, we obtain a classification accuracy of 81.7% when we use the combined features with a support vector machines (SVM) classifier.\",\"PeriodicalId\":158728,\"journal\":{\"name\":\"2020 24th International Computer Science and Engineering Conference (ICSEC)\",\"volume\":\"107 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 24th International Computer Science and Engineering Conference (ICSEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSEC51790.2020.9375240\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 24th International Computer Science and Engineering Conference (ICSEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSEC51790.2020.9375240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Drowsiness Detection using Facial Emotions and Eye Aspect Ratios
Drowsy drivers are a major cause of many road accidents around the world. Facial emotions are known to be one of the visual cues for detecting drowsiness. In this paper, we propose a machine learning approach to drowsiness detection based on using a combination of facial emotion features extracted by using deep convolutional neural networks (CNN) and eye-aspect-ratio (EAR) features. The combined feature vectors are then used for training a classifier. From our experiments, we obtain a classification accuracy of 81.7% when we use the combined features with a support vector machines (SVM) classifier.