Real-time facial pose estimation and tracking by coarse-to-fine iterative optimization

We present a novel and efficient method for real-time multiple facial poses estimation and tracking in a single frame or video. First, we combine two standard convolutional neural network models for face detection and mean shape learning to generate initial estimations of alignment and pose. Then, we design a bi-objective optimization strategy to iteratively refine the obtained estimations. This strategy achieves faster speed and more accurate outputs. Finally, we further apply algebraic filtering processing, including Gaussian filter for background removal and extended Kalman filter for target prediction, to maintain real-time tracking superiority. Only general RGB photos or videos are required, which are captured by a commodity monocular camera without any priori or label. We demonstrate the advantages of our approach by comparing it with the most recent work in terms of performance and accuracy.