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Facial action unit (FAU) intensities are popular descriptors for the analysisof facial behavior. However, FAUs are sparsely represented when only a few areactivated at a time. In this study, we explore the possibility of representingthe dynamics of facial expressions by adopting algorithms used for wordrepresentation in natural language processing. Specifically, we performclustering on a large dataset of temporal facial expressions with 5.3M framesbefore applying the Global Vector representation (GloVe) algorithm to learn theembeddings of the facial clusters. We evaluate the usefulness of our learnedrepresentations on two downstream tasks: schizophrenia symptom estimation anddepression severity regression. These experimental results show the potentialeffectiveness of our approach for improving the assessment of mental healthsymptoms over baseline models that use FAU intensities alone.

Modeling Dynamics of Facial Behavior for Mental Health Assessment