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The diversity of facial shapes and motions among persons is one of thegreatest challenges for automatic analysis of facial expressions. In thispaper, we propose a feature describing expression intensity over time, whilebeing invariant to person and the type of performed expression. Our feature isa weighted combination of the dynamics of multiple points adapted to theoverall expression trajectory. We evaluate our method on several tasks allrelated to temporal analysis of facial expression. The proposed feature iscompared to a state-of-the-art method for expression intensity estimation,which it outperforms. We use our proposed feature to temporally align multiplesequences of recorded 3D facial expressions. Furthermore, we show how ourfeature can be used to reveal person-specific differences in performances offacial expressions. Additionally, we apply our feature to identify the localchanges in face video sequences based on action unit labels. For all theexperiments our feature proves to be robust against noise and outliers, makingit applicable to a variety of applications for analysis of facial movements.

Unsupervised Features for Facial Expression Intensity Estimation over Time