UPFP-SG: A New Benchmark for Unilateral Peripheral Facial Paralysis Severity Grading

Abstract

Unilateral facial palsy, a common type of facial paralysis, profoundly impacts individuals’ daily functionality and quality of life. The current clinical diagnosis of facial paralysis primarily relies on the subjective judgment of doctors, and the development of automated detection methods is challenged by the lack of publicly available facial paralysis datasets and the inability to analyze different facial nerve branches. To address these problems, we propose a new benchmark named UPFG-SG for Unilateral Peripheral Facial Paralysis Severity Grading. First, we establish a dataset with an improved subjective evaluating system to assess the palsy severity of different peripheral facial nerve branches, which can be obtained via https://www.iiplab.net/upfp-sg/. Second, we propose a new method trained on this dataset which integrates different facial features to rate the facial palsy severity of each facial nerve region. Additionally, an enhanced regression module is designed to improve the accuracy of evaluation. With these improvements, our method effectively captures both subtle facial expression changes and fine local details. Experimental results based on our dataset demonstrate that the proposed method outperforms current deep learning methods in the field.