Robust face tracking with a consumer depth camera
Fei Hua Yang, Junzhou Huang, Yu Xiang, Xinyi Cui, Dimitris Metaxas · 2012
We address the problem of tracking human faces under various poses and lighting conditions. Reliable face tracking is a challenging task. The shapes of the faces may change dramatically with various identities, poses and expressions. Moreover, poor lighting conditions may cause a low contrast image or cast shadows on faces, which will significantly degrade the performance of the tracking system. In this paper, we develop a framework to track face shapes by using both color and depth information. Since the faces in various poses lie on a nonlinear manifold, we build piecewise linear face models, each model covering a range of poses. The low-resolution depth image is captured by using Microsoft Kinect, and is used to predict head pose and generate extra constraints at the face boundary. Our experiments show that, by exploiting the depth information, the performance of the tracking system is significantly improved.