Localization of human faces fusing color segmentation and depth from stereo

Describes a method to localize faces in color images based on the fusion of the information gathered from a stereo vision system and the analysis of color images. Our method generates a depth map of the scene and tries to fit a head model taking into account the shape of the model and skin color information. The method is tailored for its use in factory automation applications where the detection and localization of humans is necessary for the completion or interruption of a particular task, such as robot manipulator safety or the interaction of service robots with humans.

[1]  Emanuele Trucco,et al.  Rectification with unconstrained stereo geometry , 1997, BMVC.

[2]  O. Faugeras Three-dimensional computer vision: a geometric viewpoint , 1993 .

[3]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[5]  Carlo Tomasi Camera Calibration , 2002 .

[6]  Thomas S. Huang,et al.  Human face detection in a complex background , 1994, Pattern Recognit..

[7]  Takeo Kanade,et al.  Rotation Invariant Neural Network-Based Face Detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[8]  A. Sanfeliu,et al.  Object tracking system using colour histograms * , 2001 .

[9]  Ioannis Pitas,et al.  Face localization and facial feature extraction based on shape and color information , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[10]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[11]  Juan Andrade-Cetto,et al.  MARCO: A mobile robot with learning capabilities to perceive and interact with its environment* , 2001 .

[12]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).