  Facial nerve paresis is a severe complication that arises post-head and neck
surgery; This results in articulation problems, facial asymmetry, and severe
problems in non-verbal communication. To overcome the side effects of
post-surgery facial paralysis, rehabilitation requires which last for several
weeks. This paper discusses an unsupervised approach to rehabilitating patients
who have temporary facial paralysis due to damage in mimetic muscles. The work
aims to make the rehabilitation process objective compared to the current
subjective approach, such as House-Brackmann (HB) scale. Also, the approach
will assist clinicians by reducing their workload in assessing the improvement
during rehabilitation. This paper focuses on the clustering approach to monitor
the rehabilitation process. We compare the results obtained from different
clustering algorithms on various forms of the same data set, namely dynamic
form, data expressed as functional data using B-spline basis expansion, and by
finding the functional principal components of the functional data. The study
contains data set of 85 distinct patients with 120 measurements obtained using
a Kinect stereo-vision camera. The method distinguish effectively between
patients with the least and greatest degree of facial paralysis, however
patients with adjacent degrees of paralysis provide some challenges. In
addition, we compared the cluster results to the HB scale outputs.
