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Emotion recognition from embedded bodily expressions and speech during dyadic interactions

机译：在二等互动期间从嵌入的身体表达和语音的情感识别

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Previous work on emotion recognition from bodily expressions focused on analysing such expressions in isolation, of individuals or in controlled settings, from a single camera view, or required intrusive motion tracking equipment. We study the problem of emotion recognition from bodily expressions and speech during dyadic (person-person) interactions in a real kitchen instrumented with ambient cameras and microphones. We specifically focus on bodily expressions that are embedded in regular interactions and background activities and recorded without human augmentation to increase naturalness of the expressions. We present a human-validated dataset that contains 224 high-resolution, multi-view video clips and audio recordings of emotionally charged interactions between eight couples of actors. The dataset is fully annotated with categorical labels for four basic emotions (anger, happiness, sadness, and surprise) and continuous labels for valence, activation, power, and anticipation provided by five annotators for each actor. We evaluate vision and audio-based emotion recognition using dense trajectories and a standard audio pipeline and provide insights into the importance of different body parts and audio features for emotion recognition.
机译：以前的工作来自身体表达的情感识别，重点是分析单独的个人或受控设置中的这种表达，从单个相机视图或所需的侵入式运动跟踪设备。我们研究了在用环境相机和麦克风的真正厨房的二级（人类人）交互中的身体表达和言语的情感认知问题。我们专注于嵌入在常规交互和背景活动中的身体表达，并记录没有人类增强以增加表达的自然。我们介绍了一个人的验证数据集，其中包含224个高分辨率，多视图视频剪辑和音频录制的八对演员之间的情绪充电的相互作用。 DataSet完全注释，有四种基本情绪（愤怒，幸福，悲伤和惊喜）和持续标签，可为每个演员提供的五个注释器提供的价值，激活，动力和预期的连续标签。我们使用致密的轨迹和标准音频管道评估基于视觉和音频的情感识别，并为情感识别的不同身体部位和音频功能的重要性提供了解。

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