...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of human-computer studies >Observers' physiological measures in response to videos can be used to detect genuine smiles
【24h】

Observers' physiological measures in response to videos can be used to detect genuine smiles

机译：观察者的生理措施响应视频可用于检测真正的笑容

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We investigated a method to detect genuine smiles from observers' physiological states. We recorded two physiological measures from people observing videos of smiles: pupillary response (PR) and galvanic skin response (GSR). Smile videos were from two benchmark databases (MAHNOB and AFEW). MAHNOB videos were classified as showing genuine or real smiles and AFEW videos were classified as not showing real smiles, based on their process of elicitation. A leave-one-observer-out procedure was employed to investigate classification performance using k-nearest neighbor (KNN), support vector machine (SVM), simple neural network (NN), and ensemble classifiers. Different noise removal techniques and a feature selection method canonical correlation analysis with neural network (NCCA) were applied to find minimally correlated features for the classes. Using these methods, the highest classification accuracy of 97.8% for PR and 96.6% for GSR signals were found via the ensemble classifier. In comparison, the observers (n = 20) correctly judged smiles as real only 58.9% of the time (on average) to 68.4% (by voting), which is similar to the literature, showing our data is similar in quality. Overall, our results demonstrate that user-independent analyses of physiological measures can substantially outperform individual self-reports for detecting real smiles.
机译：我们调查了一种从观察者的生理国家检测真正微笑的方法。我们从观察微笑视频的人中记录了两种生理措施：瞳孔反应（PR）和电致皮肤反应（GSR）。微笑视频来自两个基准数据库（Mahnob和Afew）。 Mahnob视频被归类为显示真正或真正的微笑，并且基于他们的委托过程，Abriew视频被归类为没有表现出真正的微笑。采用休假手续程序来研究使用K-Collect邻（KNN），支持向量机（SVM），简单的神经网络（NN）和集合分类器来调查分类性能。应用了不同的噪声去除技术和特征选择方法，具有神经网络（NCCA）的规范相关分析，用于查找类别的微小相关特征。使用这些方法，PR的最高分类精度为97.8％，并通过集合分类器找到了GSR信号的96.6％。相比之下，观察者（n = 20）正确地判断微笑，只有58.9％的时间（平均）到68.4％（通过投票），这与文献类似，显示我们的数据质量相似。总体而言，我们的结果表明，用户无关地分析生理措施的分析可以大大倾向于检测真实微笑的单个自我报告。

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