3D Head Pose Estimation Using Tensor Decomposition and Non-linear Manifold Modeling

摘要

Head pose estimation is a challenging computer vision problem with important applications in different scenarios such as human-computer interaction or face recognition. In this paper, we present an algorithm for 3D head pose estimation using only depth information from Kinect sensors. A key feature of the proposed approach is that it allows modeling the underlying 3D manifold that results from the combination of pitch, yaw and roll variations. To do so, we use tensor decomposition to generate separate subspaces for each variation factor and show that each of them has a clear structure that can be modeled with cosine functions from a unique shared parameter per angle. Such representation provides a deep understanding of data behavior and angle estimations can be performed by optimizing combination of these cosine functions. We evaluate our approach on two publicly available databases, and achieve top state-of-the-art performance.