Computer vision architecture for real-time face and hand detection and tracking

  • Authors:

  • D. González-Ortega;F. J. Díaz-Pernas;J. F. Díez-Higuera;M. Martínez-Zarzuela;D. Boto-Giralda

  • Affiliations:

  • Department of Signal Theory, Communications and Telematics Engineering, School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain;Department of Signal Theory, Communications and Telematics Engineering, School of Telecommunications Engineering, University of Valladolid, Valladolid, Spain

  • Venue:
  • VISUAL'05 Proceedings of the 8th international conference on Visual Information and Information Systems
  • Year:
  • 2005

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Abstract

In this paper we present a computer vision architecture to detect and track the face and hands of a human being in real time from a video sequence captured by a webcam. The architecture has a first preprocessing stage, including a color filtering module, a motion filtering module, a color-based segmentation, a processing channels merge module and, finally, a contour search and discrimination module. The aim of the first stage is to discard the image regions which are highly unlikely to correspond with skin. Thus, the second stage of the architecture is a previously trained Fuzzy ARTMAP multiscale neural network module which only processes those image regions selected by the preprocessing stage, which are fully expected to be skin. The neural networks make the last decision about face and hand detection. After that, the architecture tracks the trajectories which face and hands follow.