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Head Motion Modeling for Human Behavior Analysis in Dyadic Interaction

机译：双向交互中人类行为分析的头部动作建模

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This paper presents a computational study of head motion in human interaction, notably of its role in conveying interlocutors’ behavioral characteristics. Head motion is physically complex and carries rich information; current modeling approaches based on visual signals, however , are still limited in their ability to adequately capture these important properties. Guided by the methodology of kinesics , we propose a data-driven approach to identify typical head motion patterns. The approach follows the steps of first segmenting motion events, then parametrically representing the motion by linear predictive features, and finally generalizing the motion types using Gaussian mixture models. The proposed approach is experimentally validated using video recordings of communication sessions from real couples involved in a couples therapy study. In particular we use the head motion model to classify binarized expert judgments of the interactants’ specific behavioral characteristics where entrainment in head motion is hypothesized to play a role: Acceptance, Blame, Positive, and Negative behavior. We achieve accuracies in the range of 60% to 70% for the various experimental settings and conditions. In addition, we describe a measure of motion similarity between the interaction partners based on the proposed model. We show that the relative change of head motion similarity during the interaction significantly correlates with the expert judgments of the interactants’ behavioral characteristics. These findings demonstrate the effectiveness of the proposed head motion model, and underscore the promise of analyzing human behavioral characteristics through signal processing methods.
机译：本文介绍了人类动作中头部运动的计算研究，特别是头部运动在传达对话者行为特征方面的作用。头部运动在身体上很复杂，并且携带丰富的信息。但是，当前基于视觉信号的建模方法仍然无法充分捕获这些重要属性。在运动学方法学的指导下，我们提出了一种数据驱动的方法来识别典型的头部运动模式。该方法遵循以下步骤：首先对运动事件进行分段，然后通过线性预测特征​​参数化地表示运动，最后使用高斯混合模型来概括运动类型。所提议的方法通过使用涉及夫妇疗法研究的真实夫妇的交流会话的视频记录进行了实验验证。尤其是，我们使用头部运动模型对交互作用者特定行为特征的二值化专家判断进行分类，其中假设头部运动会产生影响：接受，责备，积极和消极行为。对于各种实验设置和条件，我们都能达到60％至70％的精度。此外，我们基于提出的模型描述了交互伙伴之间的运动相似性度量。我们发现，互动过程中头部动作相似度的相对变化与互动者行为特征的专家判断显着相关。这些发现证明了所提出的头部运动模型的有效性，并强调了通过信号处理方法分析人类行为特征的希望。

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