Abstract
Head pose is an important cue in computer vision when using facial information. Over the last three decades, methods for head pose estimation have received increasing attention due to their application in several image analysis tasks. Although many techniques have been developed in the years to address this issue, head pose estimation remains an open research topic, particularly in unconstrained environments. In this paper, we present a comprehensive survey focusing on methods under both constrained and unconstrained conditions, focusing on the literature from the last decade. This work illustrates advantages and disadvantages of existing algorithms, starting from seminal contributions to head pose estimation, and ending with the more recent approaches which adopted deep learning frameworks. Several performance comparison are provided. This paper also states promising directions for future research on the topic.