Comparative Learning Applied to Intensity Rating of Facial Expressions of Pain

Together with classification of facial expressions, the rating of their intensities is of major interest. Classical supervised learning techniques require labeling of the intensities, which is labor intensive and requires expert knowledge, but nevertheless is not guaranteed to be objective. We propose a new approach to learn an intensity rating function which does not require expert knowledge, because it simplifies the labeling task by avoiding the difficulty of selecting an absolute intensity value and to keep the labeling consistent for the whole dataset. It is based on a novel kind of ground truth which we call Comparative Labeling. It specifies sample pairs for which the first element is desired to have a lower intensity than the second. We introduce a learning scheme to find an optimal intensity function in respect of the Comparative Labeling and propose performance measures to assess the quality of the learned function. The technique is applied to rate the intensity of facial expressions of posed pain. The evaluation results show that the learned function is well suited for determining dynamic intensity variation over time. We also assess the suitability of the rating as an inter-individual intensity measure by comparing it to the intensity ratings given by human observers.