Abstract
Assessing facial nerve function from visible facial signs such as resting asymmetry and symmetry of voluntary movement is an important means in clinical practice. By using image processing, computer vision and machine learning techniques, replacing the clinician with a machine to do assessment from ubiquitous visual face capture is progressing more closely to reality. This approach can do assessment in a purely automated manner, hence opens a promising direction for future development in this field. Many studies gathered around this interesting topic with a variety of solutions proposed in recent years. However, to date, none of these solutions have gained a widespread clinical use. This study provides a comprehensive review of the most relevant and representative studies in automated facial nerve function assessment from visual face capture, aiming at identifying the principal challenges in this field and thus indicating directions for future work.
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