Head Pose Estimation by Instance Parameterization

Head pose estimation from images is a challenging task with extensive applications. It has been attracting research attentions over decades and numerous approaches have been proposed. Among them, manifold embedding based methods, which assume that the pose variations lie on a low-dimensional manifold embedded in the high-dimensional feature space, have achieved great success. However, previous manifold embedding based methods have two drawbacks: first, they lack the capability to simultaneously deal with multiple pose-unrelated factors in a uniform way, second, they suffer from limited representation ability for out-of-sample testing inputs. In this paper we propose a novel head pose estimation method to address these problems. By learning the mapping from a uniform geometry representation to individual instance manifolds, this approach allows us to parameterize various pose-unrelated factors under a uniform framework. Our approach is a generative model which guarantees the reasonable and effective representation of new testing input. Besides, by employing eigen instance bases instead of full instance bases arisen from the training data, we can effectively compact the size of the trained model and significantly simplify the computational complexity of the testing process. Experiments on public databases such as CMU-MultiPIE and BU-4DFE, and quantitative comparisons with other state-of-the-art methods show the effectiveness of our approach.