• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems >Emotion-specific Dichotomous Classification and Feature-level Fusion of Multichannel Biosignals for Automatic Emotion Recognition
【24h】

Emotion-specific Dichotomous Classification and Feature-level Fusion of Multichannel Biosignals for Automatic Emotion Recognition

机译：多声道生物资料的情感特定的二分法分类和特征级融合，用于自动情感识别

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Endowing the computer with the ability to recognize human emotional states is the most important prerequisites for realizing an affect-sensitive human-computer interaction. In this paper, we deal with all the essential stages of an automatic emotion recognition system using multichannel physiological measures, from data collection to the classification process. Particularly we develop two different classification methods, feature-level fusion and emotion-specific classification scheme. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity, and respiration changes while subjects were listening to music. A wide range of physiological features from various analysis domains is proposed to correlate them with emotional states. Classification of four musical emotions is performed by using feature-level fusion combined with an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we developed a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA feature-level fusion. Improved recognition accuracy of 95% and 70% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.
机译：赋予了能力识别人类情绪状态的能力是实现影响敏感的人机互动的最重要的先决条件。在本文中，我们处理使用多通道生理措施，从数据收集到分类过程的所有基本阶段。特别是我们开发了两种不同的分类方法，特征级融合和特定于情绪的分类方案。使用四通道生物传感器来测量电灰度，心电图，皮肤导电性​​和呼吸变化，而受试者正在听音乐。提出了各种分析域的各种生理特征，以与情绪状态相关联。通过使用具有扩展线性判别分析（PLDA）的特征级融合来进行四种音乐情绪的分类。此外，通过利用2D情感模型的二分性质，我们开发了一种新颖的特定于情感多级二分法分类方案（EMDC），并使用PLDA特征级融合进行直接多字符分类对其性能进行比较。通过使用EMDC方案，分别提高了受试者依赖性和主题分类的95％和70％的识别准确度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号