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首页> 外文期刊>Affective Computing, IEEE Transactions on >Multimodal Emotion Recognition in Response to Videos
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Multimodal Emotion Recognition in Response to Videos

机译：响应视频的多模式情感识别

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摘要

This paper presents a user-independent emotion recognition method with the goal of recovering affective tags for videos using electroencephalogram (EEG), pupillary response and gaze distance. We first selected 20 video clips with extrinsic emotional content from movies and online resources. Then, EEG responses and eye gaze data were recorded from 24 participants while watching emotional video clips. Ground truth was defined based on the median arousal and valence scores given to clips in a preliminary study using an online questionnaire. Based on the participants' responses, three classes for each dimension were defined. The arousal classes were calm, medium aroused, and activated and the valence classes were unpleasant, neutral, and pleasant. One of the three affective labels of either valence or arousal was determined by classification of bodily responses. A one-participant-out cross validation was employed to investigate the classification performance in a user-independent approach. The best classification accuracies of 68.5 percent for three labels of valence and 76.4 percent for three labels of arousal were obtained using a modality fusion strategy and a support vector machine. The results over a population of 24 participants demonstrate that user-independent emotion recognition can outperform individual self-reports for arousal assessments and do not underperform for valence assessments.
机译：本文提出了一种与用户无关的情感识别方法，旨在利用脑电图（EEG），瞳孔反应和注视距离恢复视频的情感标签。我们首先从电影和在线资源中选择了20个具有外在情感内容的视频剪辑。然后，在观看情感视频剪辑时，记录了24名参与者的脑电图反应和视线数据。地面真相是根据使用在线问卷在初步研究中赋予片段的中位数唤醒和化合分数定义的。根据参与者的回答，为每个维度定义了三个类别。唤醒类是平静的，中等引起的和活跃的，价类是不愉快的，中立的和令人愉快的。通过身体反应的分类确定价或唤醒的三个情感标签之一。采用单参与者交叉验证的方法，以用户独立的方式调查分类性能。使用模态融合策略和支持向量机，三个价位标记的最佳分类准确度为68.5％，三个价位唤醒的最佳分类准确度为76.4％。在24位参与者的总体研究结果中，与用户无关的情绪识别在唤醒评估方面的表现优于个人自我报告，而在效价评估方面却不逊色。

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