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.

使用面部信息时，头部姿势是计算机视觉中的一个重要提示。在过去的三十年中，头部姿势估计方法由于在多个图像分析任务中的应用而受到越来越多的关注。尽管多年来已经开发了许多技术来解决这个问题，但头部姿势估计仍然是一个开放的研究课题，特别是在不受约束的环境中。在本文中，我们对受约束和不受约束条件下的方法进行了全面调查，重点关注过去十年的文献。这项工作说明了现有算法的优缺点，从对头部姿势估计的开创性贡献开始，到采用深度学习框架的最近方法结束。提供了几个性能比较。