Summary: Existing automated objective grading systems either fail to consider the face’s complex three-dimensional morphology or have poor feasibility and usability. Consumer-based red, green, and blue depth sensors or smartphone integrated three-dimensional hardware can inexpensively collect detailed four-dimensional facial data in real time but are yet to be incorporated into a practical system. This study aimed to evaluate the feasibility of a proof-of-concept automated four-dimensional facial assessment system using a red/green/blue depth sensor (OpenFAS) for use in a standard clinical environment. This study was performed on healthy adult volunteers and patients with facial nerve palsy. The setup consists of the Intel (Santa Clara, Calif.) RealSense SR300 connected to a laptop running the OpenFAS application. The subject sequentially mimics the facial expressions shown on screen. Each frame is landmarked and automatic anthropometric calculations are performed. Any errors during each session were noted. Landmarking accuracy was estimated by comparing the ground-truth position of landmarks annotated manually with those placed automatically. Eighteen participants were included in the study, nine healthy participants and nine patients with facial nerve palsy. Each session was standardized at approximately 106 seconds. A total of 61.8 percent of landmarks were automatically annotated within approximately 1.575 mm of their ground-truth locations. The findings support that OpenFAS is usable and feasible in routine settings, laying down the critical groundwork for a facial assessment system that addresses the shortcomings of existing tools. The iteration of OpenFAS presented in this study is nascent; future work, including improvements to landmarking accuracy, analyses components, and red/green/blue depth technology, is required before clinical application.