In this paper, an effective pipeline to automatic 4D Facial Expression Recognition (4D FER) is proposed. It combines two growing but disparate ideas in Computer Vision -- computing the spatial facial deformations using tools from Riemannian geometry and magnifying them using temporal filtering. The flow of 3D faces is first analyzed to capture the spatial deformations based on the recently-developed Riemannian approach, where registration and comparison of neighboring 3D faces are led jointly. Then, the obtained temporal evolution of these deformations are fed into a magnification method in order to amplify the facial activities over the time. The latter, main contribution of this paper, allows revealing subtle (hidden) deformations which enhance the emotion classification performance. We evaluated our approach on BU-4DFE dataset, the state-of-art 94.18% average performance and an improvement that exceeds 10% in classification accuracy, after magnifying extracted geometric features (deformations), are achieved.

中文翻译：

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放大细微的面部动作，实现有效的4D表情识别

在本文中，提出了一种有效的自动4D面部表情识别（4D FER）管道。它结合了计算机视觉中两个正在发展但又截然不同的想法-使用黎曼几何学中的工具计算空间面部变形，并使用时间滤波将其放大。首先基于最近开发的黎曼方法分析3D面的流动以捕获空间变形，在该方法中联合引导和比较相邻3D面。然后，将获得的这些变形的时间演变输入到放大方法中，以便随时间放大面部活动。后者是本文的主要贡献，可以揭示细微（隐藏）的变形，从而增强了情感分类的性能。我们在BU-4DFE数据集上评估了我们的方法，