Parkinson's disease (PD) is a neurodegenerative disease which is prevalent among the elder population and severely affects the life quality of patients and their families. Therefore, it is important to conduct an early diagnosis for potential patients with PD, so as to promote prompt treatment and avoid the aggravation of the disease. Recently, the in-vitro PD diagnosis based on facial expressions has received increasing attention because of its distinguishability (i.e., PD patients always possess the characteristics of “masked face”) and affordability. However, the performance of the existing facial expression-based PD diagnosis approaches is limited by: 1) the small-scale training data on PD patients' facial expressions, and 2) the weak prediction model. To address these two problems, we propose a new facial expression guided PD diagnosis method based on high-quality training data augmentation and deep neural network prediction. Specifically, the proposed method consists of three stages: Firstly, we synthesize virtual facial expression images with 6 basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) based on multi-domain adversarial learning to approximate the premorbid expressions of PD patients. Secondly, we introduce three facial image quality assessment (FIQA) criteria to measure the quality of these synthesized facial expression images and design a fusion screening strategy that shortlists the high-quality ones to augment the training data. Finally, we train a deep neural network prediction model based on the original and synthesized high-quality facial expression images for PD diagnosis. To show real-world impacts and evaluate the proposed method under different facial expressions, we also create a (currently largest) multiple facial expressions-based PD face dataset in collaboration with a hospital. Extensive experiments are performed to demonstrate the effectiveness of the multi-domain adversarial learning-based facial expression synthesis and the fusion screening strategy, particularly the superior performance of the proposed method for PD diagnosis.
- Parkinson's disease diagnosis
- data augmentation
- multi-domain adversarial learning
- deep learning