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The Weka suite was selected to design the classifier and implement the learning algorithm. Our facial measures aim to identify asymmetry levels within the face elements using facial landmarks, and a binary classifier based on a multi-layer perceptron approach provides an output label. A system consisting of three modules-facial landmark extraction, facial measure computation, and facial paralysis classification-was designed. In this work, a methodology to detect facial paralysis in a face photograph is proposed. Computer-based systems as a means to detect facial paralysis are important in the development of standardized tools for medical assessment, treatment, and monitoring additionally, they are expected to provide user-friendly tools for patient monitoring at home. The well-being of the patient could also be negatively affected.
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The inability to move the muscles of the face on one or both sides is known as facial paralysis, which may affect the ability of the patient to speak, blink, swallow saliva, eat, or communicate through natural facial expressions.
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