Automatic stress detection evaluating models of facial action units

Emotional stress detection can be performed analyzing different facial parameters. This paper focuses on the automated identification of facial Action Units (AU) as quantitative indices in order to discriminate between neutral and stress/anxiety state. Thus, a model for automatic recognition of facial action units is proposed being trained in two available annotated facial datasets, the UNBC and the BOSPHORUS datasets. Facial features, both geometric (non-rigid deformations of 3D shape of AAM landmarks) and appearance (Histograms of Oriented Gradients) were extracted. The intensity of each AU was regressed using Support Vector Regression (SVR). The corresponding models of each dataset were fused to a combined model. This combined model was applied to the experimental dataset (SRD'15) containing neutral states and inducing stressful states related to four types of stress. The results indicate that there are specific AU relevant to stress and the AU intensity are significant increased during stress leading to a more expressive human face.