Facial Action Unit Intensity Estimation And Feature Relevance Visualization With Random Regression Forests

Automatic facial action unit intensity estimation can be useful for various applications in affective computing. In this paper, we apply random regression forests for this task and propose modifications that improve predictive performance compared to the original random forest. Further, we introduce a way to estimate and visualize the relevance of the features for an individual prediction and the forest in general. We conduct experiments on the FERA 2017 challenge dataset (which outperform the FERA baseline results), show the performance gain by the modifications, and illustrate feature relevance.