Detecting Paralysis of Stroke Symptom in Video: Transfer Learning with Gated Recurrent Unit using Public Big Data of Facial Images

Sohee Ban, Hyo Suk Nam, Eunjeong Park

Abstract: This paper proposes transfer learning with spatiotemporal feature analysis using public facial images to build an automatic detection of facial paralysis caused by acute stroke. The overall process includes 1) facial detection and alignment network to extract major regions from the face, 2) transfer learning with feature extraction networks and gated recurrent unit, and 3) a classifier evaluating facial paralysis. We leveraged the Korean facial image data (K-FACE) from the public AI Hub to compensate for the insufficient data representing acute stroke symptoms. The experiment analyzed the effect of transfer learning and time series analysis using a gated recurrent unit with the deep learning models based on MobileNetV2, VGG16, and DenseNet121. Utilizing a facial big data system, transfer learning with spatiotemporal features showed a prominent performance with an accuracy of 0.925 and AUC of 0.924, which indicates the feasibility of real-time detection of stroke in daily living.