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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 PCAdoi:10.1109/icip.2013.6738750 dblp:conf/icip/KimPK13 fatcat:khzlzsryuzalvoodxnnkfcllae