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Recently, a successful pose estimation algorithm, called Cascade PoseRegression (CPR), was proposed in the literature. Trained over Pose IndexFeature, CPR is a regressor ensemble that is similar to Boosting. In this paperwe show how CPR can be represented as a Neural Network. Specifically, we adopta Graph Transformer Network (GTN) representation and accordingly train CPR withBack Propagation (BP) that permits globally tuning. In contrast, previous CPRliterature only took a layer wise training without any post fine tuning. Weempirically show that global training with BP outperforms layer-wise(pre-)training. Our CPR-GTN adopts a Multi Layer Percetron as the regressor,which utilized sparse connection to learn local image feature representation.We tested the proposed CPR-GTN on 2D face pose estimation problem as inprevious CPR literature. Besides, we also investigated the possibility ofextending CPR-GTN to 3D pose estimation by doing experiments using 3D ComputedTomography dataset for heart segmentation.

Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images