Bell’s palsy is a disease that occurs primarily between ages of 15 and 60, especially in middle-aged individuals. Although this disease gradually recovers within weeks to months, recurrence and permanent sequelae are possible. Its causes are diverse and unclear. Appropriate treatment is unknown, threatening lives of patients with this condition. In this study, we measured the degree of facial paralysis in a model of Bell’s palsy patients using OpenCV and the H.B grade measurement method and classified measured values according to H.B grade classification. This enabled prediction of the type and risk of diseases that might occur depending on the degree of facial paralysis. Additionally, we utilized more coordinate data to confirm movement of facial muscles by region to address limitations of the Nottingham system measurement method. We graded the level of this movement to enable intuitive confirmation and confirmed differences between existing Nottingham system and the H.B grade. This simple system could determine the level of paralysis in patients with Bell’s palsy and their corresponding risk level for related diseases. It enables information on causative disease of patients with Bell’s palsy to be quickly obtained, enabling prompt treatment and support.