Detection of Facial Paralysis in Human Beings Using Machine Learning Clustering

Facial paralysis occurs when a person loses the capacity to move the facial muscles on one or both sides of their face. This can happen on any side of the face. Because of this, it may be challenging for them to eat, blink, speak, or communicate through natural facial expressions. Several distinct medical diseases have been linked to the occurrence of facial paralysis in some patients. In addition to this, there is a possibility that the patient's health will deteriorate as a direct result of the circumstance. There is a need for medical tools that are standardized in terms of their examination, treatment, and monitoring. In addition to this, it is anticipated that these computer-based systems would provide patient monitoring tools that are simple to operate and can be carried out in the patient's home. In this paper, we use machine learning algorithm namely K means clustering to cluster the variables to detect the facial points in an image. The model is evaluated using a 10 fold cross validation and it is conducted in python. The result of simulation shows that the proposed method achieves higher rate of accuracy than other methods.