Using depth information to improve face detection

The information available to a robot through a variety of sensors and contextual awareness is rich and unique. In this paper, we have argued that depth and context can improve frontal face detection, in turn improving the ability of robots to interact with humans, and supported this claim with encouraging preliminary experimental results. As future work, we will attempt to apply the same concepts to the much more difficult problem of detecting faces in profile, further expanding the population with which a robot can interact.

[1]  Richard Szeliski,et al.  High-accuracy stereo depth maps using structured light , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[3]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[4]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[5]  Robert Pless,et al.  Faster and more accurate face detection on mobile robots using geometric constraints , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.