  Pain assessment through observational pain scales is necessary for special
categories of patients such as neonates, patients with dementia, critically ill
patients, etc. The recently introduced Prkachin-Solomon score allows pain
assessment directly from facial images opening the path for multiple assistive
applications. In this paper, we introduce the Histograms of Topographical (HoT)
features, which are a generalization of the topographical primal sketch, for
the description of the face parts contributing to the mentioned score. We
propose a semi-supervised, clustering oriented self--taught learning procedure
developed on the emotion oriented Cohn-Kanade database. We use this procedure
to improve the discrimination between different pain intensity levels and the
generalization with respect to the monitored persons, while testing on the UNBC
McMaster Shoulder Pain database.
