Dominance detection in face-to-face conversations

Sergio Escalera, R. M. Martinez, Jordi Vitrià, P. Radeva, M. Anguera
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引用次数: 8

Abstract

Dominance is referred to the level of influence a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on dominance detection from visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers opinion. Moreover, the considered indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analysis shows a high correlation and allows the categorization of dominant people in public discussion video sequences.
面对面对话中的优势侦测
支配力指的是一个人在谈话中的影响力。支配地位是社会心理学的一个重要研究领域,但支配地位的自动估计问题是最近在社交和可穿戴计算环境下才出现的一个话题。本文主要研究基于视觉线索的优势检测。我们通过对一组面对面对话中的主导人物进行分类来估计观察者之间的相关性。定义不同的优势指标,手工标注,并与观察者的意见进行比较。此外,从视频序列中自动提取考虑的指标,并使用二值分类器进行学习。这三种分析的结果显示出高度的相关性,并允许对公共讨论视频序列中的主导人物进行分类。
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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