Automatic Facial Paralysis Estimation with Facial Action Units

Facial palsy is unilateral facial nerve weakness or paralysis of rapid onset with unknown causes. Automatically estimating facial palsy severeness can be helpful for the diagnosis and treatment of people suffering from it across the world. In this work, we develop and experiment with a novel model for estimating facial palsy severity. For this, an effective Facial Action Units (AU) detection technique is incorporated into our model, where AUs refer to a unique set of facial muscle movements used to describe almost every anatomically possible facial expression. In this paper, we propose a novel Adaptive Local-Global Relational Network (ALGRNet) for facial AU detection and use it to classify facial paralysis severity. ALGRNet mainly consists of three main novel structures: (i) an adaptive region learning module that learns the adaptive muscle regions based on the detected landmarks; (ii) a skip-BiLSTM that models the latent relationships among local AUs; and (iii) a feature fusion&refining module that investigates the complementary between the local and global face. Quantitative results on two AU benchmarks, i.e., BP4D and DISFA, demonstrate our ALGRNet can achieve promising AU detection accuracy. We further demonstrate the effectiveness of its application to facial paralysis estimation by migrating ALGRNet to a facial paralysis dataset collected and annotated by medical professionals.

Adaptive Local-Global Relational Network for Facial Action Units Recognition and Facial Paralysis Estimation

Facial action units (AUs) refer to a unique set of facial muscle movements at certain facial locations defined by the Facial Action Coding System (FACS), which can be used for describing nearly any anatomically possible facial expression. Many existing facial action units (AUs) recognition approaches often enhance the AU representation by combining local features from multiple independent branches, each corresponding to a different AU, which usually neglect potential mutual assistance and exclusion relationship between AU branches or simply employ a pre-defined and fixed knowledge-graph as a prior. In addition, extracting features from pre-defined AU regions of regular shapes limits the representation ability. In this paper, we propose a novel Adaptive Local-Global Relational Network (ALGRNet) for facial AU recognition and apply it to facial paralysis estimation. ALGRNet mainly consists of three novel structures, i.e., an adaptive region learning module which learns the adaptive muscle regions based on the detected landmarks, a skip-BiLSTM module which models the latent mutual assistance and exclusion relationship among local AU features, and a feature fusion\&refining module which explores the complementarity between local AUs and the whole face for the local AU refinement. In order to evaluate our proposed method, we migrated ALGRNet to a facial paralysis dataset which is collected and annotated by medical professionals. Experiments on the BP4D and DISFA AU datasets show that the proposed approach outperforms the state-of-the-art methods by a large margin. Additionally, we also demonstrated the effectiveness of the proposed ALGRNet in applications to facial paralysis estimation.