Official websites use. Share sensitive information only on official, secure websites. Correspondence: eder. Stephanie chu-amiens. Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. To address the aforementioned problems, we implemented a deep learning algorithm to assess facial movements during smiling.
Such a model was trained on a dataset that contains healthy smiles only following an anomaly detection strategy. Furthermore, a graphical user interface was developed to test its practical usage in a clinical routine.
In conclusion, we present a deep learning model, implemented on open-source software, designed to help clinicians to assess facial movements.
Keywords: anomaly detection, deep learning, long-short term memory, facial paralysis. According to Jones et al. Moreover, facial expressions are responsible for conveying emotional messages, enhancing communication, and establishing links between individuals [ 4 ].
From a clinical point of view, the analysis of facial movements is relevant for diagnosis, care, and follow-up. Primarily, this analysis provides quantitative criteria that ensure an efficient follow-up for patients with facial pathology, e.
