Leveraging Convolutional Pose Machines for Fast and Accurate Head Pose Estimation

We propose a head pose estimation framework that leverages on a recent keypoint detection model. More specifically, we apply the convolutional pose machines (CPMs) to input images, extract different types of facial keypoint features capturing appearance information and keypoint relationships, and train multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) for head pose estimation. The benefit of leveraging on the CPMs (which we apply anyway for other purposes like tracking) is that we can design highly efficient models for practical usage. We evaluate our approach on the Annotated Facial Landmarks in the Wild (AFLW) dataset and achieve competitive results with the state-of-the-art.