Head pose estimation by imperceptible structured light sensing

We describe a method of estimating head pose in space by imperceptible structured light sensing. Firstly, through elaborate pattern projection strategy and camera-projector synchronization, pattern-illuminated images of the subject and the corresponding scene-texture image are captured under imperceptible patterned illumination. 3D positions of the key facial feature points are then derived by a combined use of (1) the 2D facial feature points in the scene-texture image that are localized by AAM, and (2) the point cloud generated by structured light sensing. Eventually, the head orientation and translation are estimated by SVD of a correlation matrix that is generated from the 3D corresponding feature point pairs over the various image frames. Extensive experiments show that the proposed method is effective, accurate, and fast in 6-DOF head pose estimation, making it suitable for use in real-time applications.

[1]  Ronald Chung,et al.  Determining Both Surface Position and Orientation in Structured-Light-Based Sensing , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[3]  Ronald Chung,et al.  Use of LCD Panel for Calibrating Structured-Light-Based Range Sensing System , 2008, IEEE Transactions on Instrumentation and Measurement.

[4]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[5]  Greg Welch,et al.  The office of the future: a unified approach to image-based modeling and spatially immersive displays , 1998, SIGGRAPH.

[6]  Luc Van Gool,et al.  Real-time face pose estimation from single range images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Mohan M. Trivedi,et al.  Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness , 2010, IEEE Transactions on Intelligent Transportation Systems.

[8]  Markus H. Gross,et al.  Embedding imperceptible patterns into projected images for simultaneous acquisition and display , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.

[9]  David Fofi,et al.  A comparative survey on invisible structured light , 2004, IS&T/SPIE Electronic Imaging.

[10]  Luis M. Bergasa,et al.  Face tracking and pose estimation with automatic three-dimensional model construction , 2009 .

[11]  Joaquim Salvi,et al.  Pattern codification strategies in structured light systems , 2004, Pattern Recognit..

[12]  S. Umeyama,et al.  Least-Squares Estimation of Transformation Parameters Between Two Point Patterns , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Tsukasa Ogasawara,et al.  3D model-based 6-DOF head tracking by a single camera for human-robot interaction , 2009, 2009 IEEE International Conference on Robotics and Automation.

[14]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[15]  Trevor Darrell,et al.  Pose estimation using 3D view-based eigenspaces , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[16]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Robert B. Fisher,et al.  Estimating 3-D rigid body transformations: a comparison of four major algorithms , 1997, Machine Vision and Applications.