Robust Model-Based 3D Head Pose Estimation

Gregory P. Meyer, Shalini Gupta, Iuri Frosio, Dikpal Reddy, Jan Kautz; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3649-3657

Abstract


We introduce a method for accurate three dimensional head pose estimation using a commodity depth camera. We perform pose estimation by registering a morphable face model to the measured depth data, using a combination of particle swarm optimization (PSO) and the iterative closest point (ICP) algorithm, which minimizes a cost function that includes a 3D registration and a 2D overlap term. The pose is estimated on the fly without requiring an explicit initialization or training phase. Our method handles large pose angles and partial occlusions by dynamically adapting to the reliable visible parts of the face. It is robust and generalizes to different depth sensors without modification. On the Biwi Kinect dataset, we achieve best-in-class performance, with average angular errors of 2.1, 2.1 and 2.4 degrees for yaw, pitch, and roll, respectively, and an average translational error of 5.9 mm, while running at 6 fps on a graphics processing unit.

Related Material


[pdf]
[bibtex]
@InProceedings{Meyer_2015_ICCV,
author = {Meyer, Gregory P. and Gupta, Shalini and Frosio, Iuri and Reddy, Dikpal and Kautz, Jan},
title = {Robust Model-Based 3D Head Pose Estimation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}