Context based Gesture Modeling based on Interaction Data Mining

本研究提出了一种基于会话语境的手势识别方法。在本研究中，我们利用模式识别技术提取了与手势共同发生的非语言模式的特征，即每个参与者的言语行为、头部手势和头部方向。在实验中，我们收集了8组叙事交互数据集来评估分类性能。实验结果表明，其他参与者的手势相位特征和非语言特征提高了对叙事性演讲中使用的交际手势和其他手势的区分率为20%

This research proposed a conversation context based gesture recognition approach.In this research, we extract features of co-occurring nonverbal patterns with gestures, i.e., speech act, head gesture, and head direction of each participant, by using pattern recognition techniques. In the experiments, we collect eight group narrative interaction datasets to evaluate the classification performance. The experimental results show that gesture phase features and nonverbal features of other participants improves the performance to discriminate communicative gestures that are used in narrative speeches and other gestures from 20 %