Multimodal emotion recognition in response to videos (Extended abstract)

2015 International Conference on Affective Computing and Intelligent Interaction (ACII) Pub Date : 2015-09-21 DOI:10.1109/ACII.2015.7344615

M. Soleymani, M. Pantic, T. Pun

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引用次数: 17

Abstract

We present a user-independent emotion recognition method with the goal of detecting expected emotions or 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. The arousal classes were calm, medium aroused and activated and the valence classes were unpleasant, neutral and pleasant. A one-participant-out cross validation was employed to evaluate the classification performance in a user-independent approach. The best classification accuracy of 68.5% for three labels of valence and 76.4% 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.

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视频响应的多模态情感识别(扩展摘要)

我们提出了一种独立于用户的情感识别方法，目的是利用脑电图(EEG)、瞳孔反应和凝视距离来检测视频的预期情绪或情感标签。我们首先从电影和网络资源中选择了20个带有外在情感内容的视频片段。然后记录24名参与者在观看情感视频片段时的脑电图反应和眼球注视数据。根据初步研究中给片段的唤醒和效价分数的中位数来定义基本真相。唤醒等级为平静、中等唤醒和激活，效价等级为不愉快、中性和愉快。采用一参与者出交叉验证，以用户独立的方式评估分类性能。使用模态融合策略和支持向量机对三个效价标签的分类准确率为68.5%，对三个唤醒标签的分类准确率为76.4%。对24名参与者的研究结果表明，用户独立情绪识别在唤醒评估中可以优于个人自我报告，而在效价评估中不会表现不佳。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 International Conference on Affective Computing and Intelligent Interaction (ACII)

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