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Towards a minimal representation of affective gestures (Extended abstract)

机译：迈向情感手势的最小代表（扩展摘要）

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摘要

How efficiently decoding affective information when computational resources and sensor systems are limited? This paper presents a framework for analysis of affective behavior starting with a reduced amount of visual information related to human upper-body movements. The main goal is to individuate a minimal representation of emotional displays based on non-verbal gesture features. The GEMEP (Geneva multimodal emotion portrayals) corpus was used to validate this framework. Twelve emotions expressed by ten actors form the selected data set of emotion portrayals. Visual tracking of trajectories of head and hands was performed from a frontal and a lateral view. Postural/shape and dynamic expressive gesture features were identified and analyzed. A feature reduction procedure was carried out, resulting in a four-dimensional model of emotion expression, that effectively classified/grouped emotions according to their valence (positive, negative) and arousal (high, low). These results show that emotionally relevant information can be detected/measured/obtained from the dynamic qualities of gesture. The framework was implemented as software modules (plug-ins) extending the EyesWeb XMI Expressive Gesture Processing Library and was tested as a component for a multimodal search engine in collaboration with Google within the EU-ICT I-SEARCH project.
机译：计算资源和传感器系统有限时有效解码情感信息吗？本文介绍了一种分析情感行为，以减少与人的上半身运动有关的视觉信息。主要目标是基于非言语手势特征来为情感显示的最小代表性。 GEMEP（日内瓦多模式情感描绘）语料库用于验证此框架。十个演员表达的十二种情绪形成了所选数据集的情感描绘。从正面和横向视图执行头部和手轨迹的视觉跟踪。识别和分析姿势/形状和动态富有动态的手势特征。进行特征减少程序，导致情绪表达的四维模型，可根据其价（阳性，阴性）和唤起（高，低）有效地分类/分组情绪。这些结果表明，可以从手势的动态质量检测/测量/获得情绪相关信息。该框架实现为扩展EyesWeb XMI富有敏感手势处理库的软件模块（插件），并在eu-ICT I-Search项目中与Google协作测试为多模式搜索引擎的组件。

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