A discriminative approach to frame-by-frame head pose tracking

We present a discriminative approach to frame-by-frame head pose tracking that is robust to a wide range of illuminations and facial appearances and that is inherently immune to accuracy drift. Most previous research on head pose tracking has been validated on test datasets spanning only a small (< 20) subjects under controlled illumination conditions on continuous video sequences. In contrast, the system presented in this paper was both trained and tested on a much larger database, GENKI, spanning tens of thousands of different subjects, illuminations, and geographical locations from images on the Web. Our pose estimator achieves accuracy of 5.82deg, 5.65deg, and 2.96deg root-mean-square (RMS) error for yaw, pitch, and roll, respectively. A set of 4000 images from this dataset, labeled for pose, was collected and released for use by the research community.

[1]  Sethuraman Panchanathan,et al.  Person-Independent Head Pose Estimation Using Biased Manifold Embedding , 2008, EURASIP J. Adv. Signal Process..

[2]  Mohan M. Trivedi,et al.  An Integrated Two-Stage Framework for Robust Head Pose Estimation , 2005, AMFG.

[3]  Irfan Essa,et al.  Head Tracking Using a Textured Polygonal Model , 1998 .

[4]  Mohan M. Trivedi,et al.  Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[5]  Michael J. Black,et al.  Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  Alex Pentland,et al.  Motion regularization for model-based head tracking , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[7]  Harry Wechsler,et al.  Face pose discrimination using support vector machines (SVM) , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[8]  Wen Gao,et al.  The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[9]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

[10]  Ian R. Fasel,et al.  Towards Practical Facial Feature Detection , 2009, Int. J. Pattern Recognit. Artif. Intell..

[11]  Marco La Cascia,et al.  Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[13]  Bernhard Schölkopf,et al.  Kernel machine based learning for multi-view face detection and pose estimation , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[14]  Timothy F. Cootes,et al.  Automatic interpretation of human faces and hand gestures using flexible models. , 1995 .

[15]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[16]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[17]  Yuan Li,et al.  High-Performance Rotation Invariant Multiview Face Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Trevor Darrell,et al.  Fast pose estimation with parameter-sensitive hashing , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[19]  Yun Fu,et al.  Graph embedded analysis for head pose estimation , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[20]  Timothy F. Cootes,et al.  View-based active appearance models , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[21]  James L. Crowley,et al.  Head Pose Estimation on Low Resolution Images , 2006, CLEAR.

[22]  Kim L. Boyer,et al.  Head pose estimation using view based eigenspaces , 2002, Object recognition supported by user interaction for service robots.

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