Automatic stress detection evaluating models of facial action units
| Autor: | Anastasios Roussos, Mohammad Rami Koujan, Giorgos Giannakakis, Kostas Marias |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
05 social sciences Feature extraction 050301 education Regression analysis Pattern recognition 02 engineering and technology Facial recognition system Support vector machine Gesture recognition Face (geometry) Histogram Stress (linguistics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business 0503 education |
| Zdroj: | FG |
| DOI: | 10.1109/fg47880.2020.00129 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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