Depression is one of the most common mental disorders affecting millions of people worldwide. Developing adjunct tools aiding depression assessment is expected to impact overall health outcomes and treatment cost reduction. To this end, platforms designed for automatic and non-invasive depression assessment could help in detecting signs of the disease on a regular basis, without requiring the physical presence of a mental health professional. Despite the different approaches that can be found in the literature, both in terms of methods and algorithms, a fully satisfactory system for the automatic assessment of depression severity has not been presented as yet. This paper describes a proposed algorithm for dynamically analyzing facial expressions using robust descriptors in order to compose a novel feature selection as well as an effective classification process. Additionally a preliminary evaluation of the system is presented, by applying local curvelet binary patterns in three orthogonal planes for depression severity assessment.