abstract.tex


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\begin{abstract}
  This paper explores a novel approach for person recognition based on skeletal
  measurements. After showing that exact measurements allow for accurate
  recognition in a large dataset, we study two algorithmic approaches for
  recognition given approximate measurements. We perform a real-world
  experiment with measurements captured from the Kinect and obtain 95\%
  accuracy with three people and 85\% accuracy with five people.  Our results
  and a comparison with state-of-the-art facial recognition validate the
  viability of skeleton-based recognition.  
  \end{abstract}