  The quantification of visual affect data (e.g. face images) is essential to
build and monitor automated affect modeling systems efficiently. Considering
this, this work proposes quantified facial Temporal-expressiveness Dynamics
(TED) to quantify the expressiveness of human faces. The proposed algorithm
leverages multimodal facial features by incorporating static and dynamic
information to enable accurate measurements of facial expressiveness. We show
that TED can be used for high-level tasks such as summarization of unstructured
visual data, and expectation from and interpretation of automated affect
recognition models. To evaluate the positive impact of using TED, a case study
was conducted on spontaneous pain using the UNBC-McMaster spontaneous shoulder
pain dataset. Experimental results show the efficacy of using TED for
quantified affect analysis.
