The diversity of facial shapes and motions among persons is one of the greatest challenges for automatic analysis of facial expressions. In this paper, we propose a feature describing expression intensity over time, while being invariant to person and the type of performed expression. Our feature is a weighted combination of the dynamics of multiple points adapted to the overall expression trajectory. We evaluate our method on several tasks all related to temporal analysis of facial expression. The proposed feature is compared to a state-of-the-art method for expression intensity estimation, which it outperforms. We use our proposed feature to temporally align multiple sequences of recorded 3D facial expressions. Furthermore, we show how our feature can be used to reveal person-specific differences in performances of facial expressions. Additionally, we apply our feature to identify the local changes in face video sequences based on action unit labels. For all the experiments our feature proves to be robust against noise and outliers, making it applicable to a variety of applications for analysis of facial movements.

该研究提出了一种描述表情强度随时间变化的特征，该特征对人和表情类型不变，可用于多个面部表情分析任务，包括3D面部表情的时间对齐、个体间表情绩效差异的识别和基于行为单元标签的面部视频序列的本地变化识别，并表现出对噪音和异常值具有鲁棒性。

基于无监督特征的面部表情强度随时间变化的估计