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Diverse videos synthesis using manifold-based parametric motion model for facial understanding

机译：使用基于流形的参数化运动模型进行脸部理解的各种视频合成

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Personal style is the cause of interpersonal variations, which play an important role in facial expression recognition. In this study, a model has been proposed to generate diverse sequences of virtual samples for a new subject. These sequences are thought to enrich the training set in order to increase robustness of recognition with respect to individual variations and improve generalisation to the new person. In the manifold-based parametric motion model, the trajectory of the source person has been used to estimate and conduct the virtual facial expression vectors of the target person. In the recognition experiments, images are represented in the lower-dimensional feature space, whereas the virtual vectors are generated in the feature space. The accuracy of the independent system shows the effectiveness of samples in improving the performance of the system based on the limited data of the new person. The accuracy of seven expressions is 90.65%, which is an improvement over the baseline model, which is 86.11%, and represents a significant ( < 0.05) improvement over the baseline method, which is 83.4%.
机译：个人风格是人际差异的原因，在面部表情识别中起着重要作用。在这项研究中，已经提出了一种模型来为新主题生成虚拟样本的各种序列。这些序列被认为可以丰富训练集，从而增强针对个体变异的识别的鲁棒性，并提高对新人的概括性。在基于流形的参数运动模型中，源人员的轨迹已用于估计和进行目标人员的虚拟面部表情矢量。在识别实验中，图像在低维特征空间中表示，而虚拟矢量在特征空间中生成。独立系统的准确性表明，基于新人员的有限数据，样本在改善系统性能方面的有效性。七个表达式的准确性为90.65％，相对于基线模型为86.11％，这是一个改进，并且表示相对于基线方法为83.4％的显着提高（<0.05）。

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