{"title":"使用深度学习的低记忆系统视频中的情绪识别","authors":"Ahmed F. Hagar, Hazem M. Abbas, M. Khalil","doi":"10.1109/ICCES48960.2019.9068168","DOIUrl":null,"url":null,"abstract":"This paper explores a deep learning model for emotion recognition in videos, suitable for systems with limited memory like robots and embedded-systems. The proposed model is a Mini-xception+LSTM architecure with around 80k parameters. This model got a classification accuracy of 93% in dinstinction between Anger and Amusement emotions using the BioVidEmo dataset, compared to 70% accuracy that a recent work got for the same two emotions, and got 86 % and 90 % classification accuracy using the CK+dataset for seven and six emotions, respectively.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Emotion Recognition In Videos For Low-Memory Systems Using Deep-Learning\",\"authors\":\"Ahmed F. Hagar, Hazem M. Abbas, M. Khalil\",\"doi\":\"10.1109/ICCES48960.2019.9068168\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores a deep learning model for emotion recognition in videos, suitable for systems with limited memory like robots and embedded-systems. The proposed model is a Mini-xception+LSTM architecure with around 80k parameters. This model got a classification accuracy of 93% in dinstinction between Anger and Amusement emotions using the BioVidEmo dataset, compared to 70% accuracy that a recent work got for the same two emotions, and got 86 % and 90 % classification accuracy using the CK+dataset for seven and six emotions, respectively.\",\"PeriodicalId\":136643,\"journal\":{\"name\":\"2019 14th International Conference on Computer Engineering and Systems (ICCES)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 14th International Conference on Computer Engineering and Systems (ICCES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCES48960.2019.9068168\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCES48960.2019.9068168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Emotion Recognition In Videos For Low-Memory Systems Using Deep-Learning
This paper explores a deep learning model for emotion recognition in videos, suitable for systems with limited memory like robots and embedded-systems. The proposed model is a Mini-xception+LSTM architecure with around 80k parameters. This model got a classification accuracy of 93% in dinstinction between Anger and Amusement emotions using the BioVidEmo dataset, compared to 70% accuracy that a recent work got for the same two emotions, and got 86 % and 90 % classification accuracy using the CK+dataset for seven and six emotions, respectively.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
