{"title":"基于Gabor小波和神经网络的电子学习系统面部表情识别","authors":"May-Ping Loh, Ya-Ping Wong, Chee-Onn Wong","doi":"10.1109/ICALT.2006.171","DOIUrl":null,"url":null,"abstract":"In this paper, we present our initial studies and results obtained on e-learning facial expression recognition using Gabor Wavelet for facial feature extraction, and Back-propagation Neural Network for expression classification. An eFEC database that consists of 600 facial expression images is built for our research. We also provide reasons on why we need to build our own database instead of customizing and use current available expressions database, and what are the differences between eFEC databases compared with others. You may find comparisons in various experiment results presented. We believe the information would be very useful as to provide guideline and direction on how to improve the system performance and make it applicable in real-life elearning environments. (Abbreviation: eFEC --learning Facial Expression Classification; cca - correct classification in average)","PeriodicalId":268199,"journal":{"name":"International Conference on Advanced Learning Technologies","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Facial Expression Recognition for E-learning Systems using Gabor Wavelet & Neural Network\",\"authors\":\"May-Ping Loh, Ya-Ping Wong, Chee-Onn Wong\",\"doi\":\"10.1109/ICALT.2006.171\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present our initial studies and results obtained on e-learning facial expression recognition using Gabor Wavelet for facial feature extraction, and Back-propagation Neural Network for expression classification. An eFEC database that consists of 600 facial expression images is built for our research. We also provide reasons on why we need to build our own database instead of customizing and use current available expressions database, and what are the differences between eFEC databases compared with others. You may find comparisons in various experiment results presented. We believe the information would be very useful as to provide guideline and direction on how to improve the system performance and make it applicable in real-life elearning environments. (Abbreviation: eFEC --learning Facial Expression Classification; cca - correct classification in average)\",\"PeriodicalId\":268199,\"journal\":{\"name\":\"International Conference on Advanced Learning Technologies\",\"volume\":\"97 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Advanced Learning Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICALT.2006.171\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Advanced Learning Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALT.2006.171","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Facial Expression Recognition for E-learning Systems using Gabor Wavelet & Neural Network
In this paper, we present our initial studies and results obtained on e-learning facial expression recognition using Gabor Wavelet for facial feature extraction, and Back-propagation Neural Network for expression classification. An eFEC database that consists of 600 facial expression images is built for our research. We also provide reasons on why we need to build our own database instead of customizing and use current available expressions database, and what are the differences between eFEC databases compared with others. You may find comparisons in various experiment results presented. We believe the information would be very useful as to provide guideline and direction on how to improve the system performance and make it applicable in real-life elearning environments. (Abbreviation: eFEC --learning Facial Expression Classification; cca - correct classification in average)