A Unified Stochastic Model for Detecting and Tracking Faces

  • Authors:

  • Sachin Gangaputra;Donald Geman

  • Affiliations:

  • The Johns Hopkins University, Baltimore, MD;The Johns Hopkins University, Baltimore, MD

  • Venue:
  • CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
  • Year:
  • 2005

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Abstract

We propose merging face detection and face tracking into a single probabilistic framework. The motivation stems from a broader project in algorithmic modeling, centered on the design and analysis of the online computational process in visual recognition. Detection is represented as a tree-structured graphical network in which likelihoods are assigned to each history or "trace" of processing, thereby introducing a new probabilistic component into coarse-to-fine search strategies. When embedded within a temporal Markov framework, the resulting tracking system yields encouraging results.