A Transfer Learning Approach for Facial Paralysis Severity Detection
Wasif Ali, Muhammad Imran, Muhammad Usman Yaseen, Khursheed Aurangzeb, Nouman Ashraf, Sheraz Aslam
Abstract
Facial paralysis is a debilitating condition that weakens or damages facial muscles resulting in asymmetric or abnormal facial movements. To aid in the diagnosis and rehabilitation of facial paralysis, researchers have developed machine learning and deep learning computer-aided diagnosis systems. However, machine learning models have limitations as they rely on facial landmark techniques and manual face palsy region extraction methods to obtain spatial information. Moreover, deep learning models need large, labelled datasets for training whereas existing available facial paralysis datasets are small and restricted. This presents significant challenges, including difficulties in data acquisition, insufficient patient numbers, and inadequate diversity within the datasets. These limitations can potentially restrict the generalizability of these models and introduce biases in the resulting outcomes. In this study, we propose an approach for the diagnosis and grading of facial paralysis comprised of two datasets, one from MEEI (Massachusetts Eye and Ear Infirmary) videos of patients and the other from the YFP (YouTube Face Palsy) dataset. The model uses a transfer learning approach to fine-tune the VGGFace model, which is pre-trained on facial images, on the prepared datasets for facial paralysis. The resultant model was subsequently renamed FP-VGGFace for the purpose of this research. Additionally, two more pre-trained models on facial images, ResNet50 and VGG16, are also fine-tuned for the facial paralysis task. This was undertaken to conduct a performance comparison of multiple models on the prepared dataset. The findings indicate that the models exhibit high accuracy, benefiting from pre-training on a diverse dataset that enables the capture of spatial information from facial images. The FP-VGGFace model achieves the best accuracy (99.3%) and F1-score (99.3%) surpassing all benchmark models. This study underscores the potential of utilizing pre-trained deep learning models for the diagnosis and rehabilitation of facial paralysis.