Robust Model-Based 3D Head Pose Estimation

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) algori...

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Bibliographic Details
Main Authors: Meyer, Gregory P., Gupta, Shalini, Frosio, Iuri, Reddy, Dikpal, Kautz, Jan
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Cameras
Computer vision
Cost function
Errors
Face
Iterative closest point algorithm
Rolling motion
Solid modeling
Swarm intelligence
Three dimensional
Three dimensional models
Three-dimensional displays
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Summary: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.
ISSN:2380-7504
DOI:10.1109/ICCV.2015.416