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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, we study two algorithmic approaches for
- identification in case of approximate measurements. A real-life
- experiment with 25 people and measurements obtained from the Kinect
- range camera gives us promising results and comparison with state of
- the art facial recognition validates the viability of
- skeleton-based identification.
-\end{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}