Facial Point Graphs for Stroke Identification

Stroke can cause significant damage to neurons, resulting in various sequelae that negatively impact the patient’s ability to perform essential daily activities such as chewing, swallowing, and verbal communication. Therefore, it is important for patients with such difficulties to undergo a treatment process and be monitored during its execution to assess the improvement of their health condition. The use of computerized tools and algorithms that can quickly and affordably detect such sequelae proves helpful in aiding the patient’s recovery. Due to the death of internal brain cells, a stroke often leads to facial paralysis, resulting in certain asymmetry between the two sides of the face. This paper focuses on analyzing this asymmetry using a deep learning method without relying on handcrafted calculations, introducing the Facial Point Graphs (FPG) model, a novel approach that excels in learning geometric information and effectively handling variations beyond the scope of manual calculations. FPG allows the model to effectively detect orofacial impairment caused by a stroke using video data. The experimental findings on the Toronto Neuroface dataset revealed the proposed approach surpassed state-of-the-art results, promising substantial advancements in this domain.