Deep Hybrid Network for Automatic Quantitative Analysis of Facial Paralysis

We propose the Deep Hybrid Network (DHN) for the analysis of facial paralysis syndrome. This is a pioneering work that explores the deep-learning for the facial paralysis study. The proposed DHN consists of three component networks, the first detects the subject's face, the second detects the landmarks and the edges on the detected faces, and the third detects the local paralysis regions. One novelty of this research is the exploration of facial edge features in the analysis of facial paralysis. Additionally, we introduce the first public database for facial paralysis study, as the previous studies were all evaluated on proprietary databases, making the comparison with other methods difficult. Our database includes 32 videos of 21 patients collected from YouTube. To enhance the robustness against expression variations, we include the CK+ facial expression database in the training and testing phases. We show that the proposed DHN does not just detect the local paralysis regions, but also captures the intensity of the syndrome over time, enabling the quantitative description of the syndrome. Experiments show that the proposed approach offers an accurate and efficient solution for facial paralysis analysis.