Estimating head pose with an RGBD sensor: A comparison of appearance-based and pose-based local subspace methods

Estimating the head pose with RGBD data when the pose is allowed to vary over a large angle remains challenging. In this paper, we show that an appearance-based construction of a set of locally optimum subspaces provides a good (fast and accurate) solution to the problem. At training time, our algorithm partitions the set of all images obtained by applying pose transformations to the 3D point cloud for a frontal view into appearance based clusters and represents each cluster with a local PCA space. Given a test RGBD images, we first find the appearance cluster that it belongs to and, subsequently, we find its pose from the training image that is closest to the test image in that cluster. Our paper compares the appearance-based local-subspace method with the pose-based local-subspace approach and with a PCA-based global subspace method. This comparison establishes the superiority of the appearance-based local-subspace approach.