Multi-view Geometry Consistency Network for Facial Micro-Expression Recognition From Various Perspectives

Gaze estimation plays an essential role in human attention recognition, human behavior analysis and augmented reality applications. Most of the deep neural network-based gaze estimation techniques apply supervised learning to extract features and regress 3D gaze vectors directly, leading to a vulnerability of high labor cost and limited generalization. In this work, we proposed a weakly-supervised method to jointly optimize the depth values of eye landmarks and relative poses with a multi-view geometric constraint to determine the final gaze vectors of observers. Specifically, we feed in sequential eye region images, and design a depth regression network to estimate the depth of the eye region landmarks, which are further utilized by the pose estimation network to estimate the relative changes of gaze vectors with multi-view geometric constraints in the iris regions. Experiments on both synthetic and real data show that the proposed method is feasible and promising to learn gaze estimation without strong pose supervision.