Introduction of a Low-Cost and Automated Four-Dimensional Assessment System of the Face

Summary: Existing automated objective grading systems either fail to consider the face’s complex 3D morphology or suffer from poor feasibility and usability. Consumer-based Red Green Blue Depth (RGB-D) sensors and/or smartphone integrated 3D hardware can inexpensively collect detailed four-dimensional facial data in real-time but are yet to be incorporated into a practical system. This study aims to evaluate the feasibility of a proof-of-concept automated 4D facial assessment system using an RGB-D sensor (termed OpenFAS) for use in a standard clinical environment. This study was performed on normal adult volunteers and patients with facial nerve palsy (FNP). The setup consists of the Intel 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 to those placed automatically. 18 participants were included in the study, nine healthy participants and nine patients with FNP. Each session was standardized at approximately 106 seconds. 61.8% of landmarks were automatically annotated within approximately 1.575mm of their ground-truth locations. Our 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. However, the iteration of OpenFAS presented in this study is undoubtedly nascent with future work including improvements to landmarking accuracy, analyses components, and RGB-D technology required before clinical application.