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A real time and robust facial expression recognition and imitation approach for affective human-robot interaction using Gabor filtering

机译:使用Gabor滤波的实时,鲁棒的面部表情识别和模仿方法,用于人机交互

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摘要

Facial expressions are a rich source of communicative information about human behavior and emotion. This paper presents a real-time system for recognition and imitation of facial expressions in the context of affective Human Robot Interaction. The proposed method achieves a fast and robust facial feature extraction based on consecutively applying filters to the gradient image. An efficient Gabor filter is used, along with a set of morphological and convolutional filters to reduce the noise and the light dependence of the image acquired by the robot. Then, a set of invariant edge-based features are extracted and used as input to a Dynamic Bayesian Network classifier in order to estimate a human emotion. The output of this classifier updates a geometric robotic head model, which is used as a bridge between the human expressiveness and the robotic head. Experimental results demonstrate the accuracy and robustness of the proposed approach compared to similar systems.
机译:面部表情是有关人类行为和情感的丰富交流信息的来源。本文提出了一种在情感性人类机器人交互作用下识别和模仿面部表情的实时系统。所提出的方法基于对梯度图像连续应用滤波器,从而实现了快速,鲁棒的面部特征提取。使用了高效的Gabor滤波器,以及一组形态学和卷积滤波器,以减少机器人获取的图像的噪声和光依赖性。然后,提取一组基于边缘的不变特征并将其用作动态贝叶斯网络分类器的输入,以便估计人类情绪。该分类器的输出更新了几何机器人头部模型,该模型被用作人类表达能力和机器人头部之间的桥梁。实验结果证明了与同类系统相比,该方法的准确性和鲁棒性。

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