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authorJon Whiteaker <jbw@berkeley.edu>2012-03-05 11:12:04 -0800
committerJon Whiteaker <jbw@berkeley.edu>2012-03-05 11:12:04 -0800
commit73834f5ef1c7be73d054baf4cf5f14a39fef17dc (patch)
tree81e50a05b90a8ad2e7b9eb781c32a44d8fe9146f /abstract.tex
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downloadkinect-73834f5ef1c7be73d054baf4cf5f14a39fef17dc.tar.gz
jon's final pass part one
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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}