{"title":"基于多特征的视频片段情感识别","authors":"Chuanhe Liu, Tianhao Tang, Kui Lv, Minghao Wang","doi":"10.1145/3242969.3264989","DOIUrl":null,"url":null,"abstract":"In this paper, we present our latest progress in Emotion Recognition techniques, which combines acoustic features and facial features in both non-temporal and temporal mode. This paper presents the details of our techniques used in the Audio-Video Emotion Recognition subtask in the 2018 Emotion Recognition in the Wild (EmotiW) Challenge. After the multimodal results fusion, our final accuracy in Acted Facial Expression in Wild (AFEW) test dataset achieves 61.87%, which is 1.53% higher than the best results last year. Such improvements prove the effectiveness of our methods.","PeriodicalId":308751,"journal":{"name":"Proceedings of the 20th ACM International Conference on Multimodal Interaction","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"89","resultStr":"{\"title\":\"Multi-Feature Based Emotion Recognition for Video Clips\",\"authors\":\"Chuanhe Liu, Tianhao Tang, Kui Lv, Minghao Wang\",\"doi\":\"10.1145/3242969.3264989\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present our latest progress in Emotion Recognition techniques, which combines acoustic features and facial features in both non-temporal and temporal mode. This paper presents the details of our techniques used in the Audio-Video Emotion Recognition subtask in the 2018 Emotion Recognition in the Wild (EmotiW) Challenge. After the multimodal results fusion, our final accuracy in Acted Facial Expression in Wild (AFEW) test dataset achieves 61.87%, which is 1.53% higher than the best results last year. Such improvements prove the effectiveness of our methods.\",\"PeriodicalId\":308751,\"journal\":{\"name\":\"Proceedings of the 20th ACM International Conference on Multimodal Interaction\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"89\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 20th ACM International Conference on Multimodal Interaction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3242969.3264989\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 20th ACM International Conference on Multimodal Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3242969.3264989","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 89
摘要
本文介绍了在非时间和时间模式下结合声音特征和面部特征的情绪识别技术的最新进展。本文介绍了我们在2018年野外情感识别挑战赛(EmotiW)中音频-视频情感识别子任务中使用的技术细节。经过多模态结果融合后，我们在act Facial Expression in Wild (AFEW)测试数据集中的最终准确率达到了61.87%，比去年的最佳结果提高了1.53%。这些改进证明了我们方法的有效性。
Multi-Feature Based Emotion Recognition for Video Clips
In this paper, we present our latest progress in Emotion Recognition techniques, which combines acoustic features and facial features in both non-temporal and temporal mode. This paper presents the details of our techniques used in the Audio-Video Emotion Recognition subtask in the 2018 Emotion Recognition in the Wild (EmotiW) Challenge. After the multimodal results fusion, our final accuracy in Acted Facial Expression in Wild (AFEW) test dataset achieves 61.87%, which is 1.53% higher than the best results last year. Such improvements prove the effectiveness of our methods.
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