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The quantified measurement of facial expressiveness is crucial to analyzehuman affective behavior at scale. Unfortunately, methods for expressivenessquantification at the video frame-level are largely unexplored, unlike thestudy of discrete expression. In this work, we propose an algorithm thatquantifies facial expressiveness using a bounded, continuous expressivenessscore using multimodal facial features, such as action units (AUs), landmarks,head pose, and gaze. The proposed algorithm more heavily weights AUs with highintensities and large temporal changes. The proposed algorithm can compute theexpressiveness in terms of discrete expression, and can be used to performtasks including facial behavior tracking and subjectivity quantification incontext. Our results on benchmark datasets show the proposed algorithm iseffective in terms of capturing temporal changes and expressiveness, measuringsubjective differences in context, and extracting useful insight.

Quantified Facial Expressiveness for Affective Behavior Analytics