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

Recently, a successful pose estimation algorithm, called Cascade Pose Regression (CPR), was proposed in the literature. Trained over Pose Index Feature, CPR is a regressor ensemble that is similar to Boosting. In this paper we show how CPR can be represented as a Neural Network. Specifically, we adopt a Graph Transformer Network (GTN) representation and accordingly train CPR with Back Propagation (BP) that permits globally tuning. In contrast, previous CPR literature only took a layer wise training without any post fine tuning. We empirically 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 in previous CPR literature. Besides, we also investigated the possibility of extending CPR-GTN to 3D pose estimation by doing experiments using 3D Computed Tomography dataset for heart segmentation.