Head pose estimation using deep neural networks and 3D point clouds

Abstract

In this paper, we propose head pose estimation using deep neural networks and 3D point cloud. Unlike existing methods that either take 2D RGB image or 2D depth image as input, we adopt 3D point cloud data generated from depth to estimate 3D head poses. To further improve robustness and accuracy of head pose estimation, we classify 3D angles of head poses into 36 classes with 5 ∘ interval and predict the probability of each angle in a class based on multi-layer perceptron (MLP). While traditional iterative methods for head model construction require high computation and memory costs, the proposed method is lightweight and computationally efficient by utilizing a sampled 3D point cloud as input combined with a graph convolutional neural network (GCNN). Experimental results on Biwi Kinect Head Pose dataset show that the proposed method achieves outstanding performance in head pose estimation and outperforms state-of-the-art ones in terms of accuracy.