Head Pose Estimation Based on Multivariate Label Distribution

Abstract

Accurate ground-truth pose is essential to the training of most existing head pose estimation methods. However, in many cases, the "ground truth" pose is obtained in rather subjective ways, such as asking the subjects to stare at different markers on the wall. Thus it is better to use soft labels rather than explicit hard labels to indicate the pose of a face image. This paper proposes to associate a multivariate label distribution (MLD) to each image. An MLD covers a neighborhood around the original pose. Labeling the images with MLD can not only alleviate the problem of inaccurate pose labels, but also boost the training examples associated to each pose without actually increasing the total amount of training examples. Four algorithms are proposed to learn from MLD. Furthermore, an extension of MLD with the hierarchical structure is proposed to deal with fine-grained head pose estimation, which is named hierarchical multivariate label distribution (HMLD). Experimental results show that the MLD-based methods perform significantly better than the compared state-of-the-art head pose estimation algorithms. Moreover, the MLD-based methods appear much more robust against the label noise in the training set than the compared baseline methods.