{"title":"基于深度通用特征和支持向量机的面部表情识别","authors":"Duc Minh Vo, T. Le","doi":"10.1109/NICS.2016.7725672","DOIUrl":null,"url":null,"abstract":"Motivated by the newly recent trend in pattern recognition - convolutional neural network (CNN), we introduce a new fusion method based on CNN and support vector machines (SVM) for facial expression recognition problem. Our study puts the deep generic features from CNN and SVM together which is more efficient than CNN only. We investigate our proposed method on Cohn-Kanade dataset and achieve 96.04% in accuracy rate which is better than other state-of-the-art methods.","PeriodicalId":347057,"journal":{"name":"2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Deep generic features and SVM for facial expression recognition\",\"authors\":\"Duc Minh Vo, T. Le\",\"doi\":\"10.1109/NICS.2016.7725672\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Motivated by the newly recent trend in pattern recognition - convolutional neural network (CNN), we introduce a new fusion method based on CNN and support vector machines (SVM) for facial expression recognition problem. Our study puts the deep generic features from CNN and SVM together which is more efficient than CNN only. We investigate our proposed method on Cohn-Kanade dataset and achieve 96.04% in accuracy rate which is better than other state-of-the-art methods.\",\"PeriodicalId\":347057,\"journal\":{\"name\":\"2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NICS.2016.7725672\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NICS.2016.7725672","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep generic features and SVM for facial expression recognition
Motivated by the newly recent trend in pattern recognition - convolutional neural network (CNN), we introduce a new fusion method based on CNN and support vector machines (SVM) for facial expression recognition problem. Our study puts the deep generic features from CNN and SVM together which is more efficient than CNN only. We investigate our proposed method on Cohn-Kanade dataset and achieve 96.04% in accuracy rate which is better than other state-of-the-art methods.
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