DRUIDE: a real-time system for robust multiple face detection, tracking and hand posture recognition in color video sequences

"DRUIDE", which stands for "detection, recognition, unification, interpretation, decision, evolution", is a novel, real-time system primarily designed for the detection and tracking of multiple human faces as well as for the simultaneous recognition of multiple hand postures in color video sequences and in complex environments. The system relies on the three fundamental cues of color, shape and motion, and integrates three mutually complementary sub-systems, in order to achieve high rates of detection, tracking and recognition. Preliminary experiments yield an average correct face detection rate of about 90%, and the additional use of tracking increases significantly the robustness of the proposed system, particularly to illumination conditions and to partial occlusions. Although we first focus specifically on human faces and hand postures, the ultimate goal of DRUIDE extends beyond human-computer interactions, to encompass the adaptive detection, tracking and recognition of various objects under unconstrained scene conditions.

[1]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[2]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Kazuhiko Yamamoto,et al.  Robust Face Detection and Japanese Sign Language Hand Posture Recognition for Human-Computer Interaction in an “ Intelligent ” Room † , 2002 .

[4]  Ian D. Reid,et al.  Self-Calibration of Rotating and Zooming Cameras , 2002, International Journal of Computer Vision.

[5]  Ian D. Reid,et al.  Zooming while Tracking Using Affine Transfer , 1996, BMVC.

[6]  Ian D. Reid,et al.  Driving saccade to pursuit using image motion , 1995, International Journal of Computer Vision.

[7]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[8]  Kazuhiko Yamamoto,et al.  Analysis of a large set of color spaces for skin pixel detection in color images , 2003, International Conference on Quality Control by Artificial Vision.

[9]  J. Horner,et al.  Phase-only matched filtering. , 1984, Applied optics.

[10]  Yajun Li,et al.  Reforming the theory of invariant moments for pattern recognition , 1992, Pattern Recognit..

[11]  Gary Bradski,et al.  Computer Vision Face Tracking For Use in a Perceptual User Interface , 1998 .