Head pose estimation: An extensive survey on recent techniques and applications

Abstract

Biometric based systems are involved in many areas, from surveillance to user authentication, from autonomous systems to human-robot interactions. Head pose estimation (HPE) is the task to support biometric systems in which any of the biometric traits of the head is involved, as face, ear or iris. This particular biometric branch finds its application in driver attention detection, surveillance for recognition, face frontalization, best frame selection and so on. The goal of HPE is to determine the head pose orientation (yaw, pitch, roll). The implemented methods use different techniques depending on the kind of input. In this survey we present an overview of involved datasets, recent techniques and applications. We evaluate and compare the different approaches with respect to their advantages and practical usage. In addition, we propose a technical comparison between training and training-free techniques for the most popular HPE methods.