Effective head pose estimation using Lie Algebrized Gaussians

Accurate head pose estimation is significant for many applications such as face recognition and human-computer interaction. In this paper, we treat the head pose estimation as a classification problem and employ the Lie Algebrized Gaussians (LAG) feature as the representation approach for head image. The LAG feature, which is built on Gausssian Mixture Model (GMM), has the capability to preserve the structure of Gaussian components in the original Lie group manifold. Moreover, to keep more spatial structure information of the image, LAG is operated on many subregions of the image. As a result, these properties of LAG enable it to reflect the pose characteristic of the head image well and possess powerful discriminative ability in pose classification. Experiments on CMU Pose, Illumination, and Expression (PIE) and Pointing'04 benchmarks show state-of-the-art performance and demonstrate that LAG represents the head pose characteristic well.