\section{Related Work}
\label{sec:related}

The related work for this paper is divided into two main sections: using
biometrics for identification and advances in skeleton mapping.

\paragraph{Biometrics}
A biometric is a distinguishable trait that can be measured and used for
identification.  There are many different possible biometrics.  One way to view
different biometrics are how intrusive they are.  More intrusive biometrics
include fingerprints, irises, and DNA; less intrusive are faces, speech, and
gait~\cite{phillips98}.  Generally, there is a tradeoff between the level of
user cooperation required and the accuracy that can be
attained~\cite{liu01,bio-survey}.

Alternatively, biometrics can be divided into physiological and behavioral
traits. Physiological traits include faces, fingerprints, and irises; speech
and gait are behavioral.  Face and gait recognition are the most relevent
biometrics for this paper as they both are unobtrusive and rely on image
processing.  Gait recognition usually involves determining the outline of a
person from an image~\cite{bio-survey}, but algorithms similar to gait
recognition have been developed for range cameras as well~\cite{gomez:hgbu11}.
Face recognition receives a lot of attention from the research
community~\cite{face-survey}, also including at least one example incorporating
a range camera~\cite{gordon91}.  Since behavioral traits typically are more
characteristic as opposed to unique for identification beyond a certain
scale~\cite{seven-issues}, we will compare the results of skeleton recognition
to face recognition.



Jain~\etal

\paragraph{Skeleton mapping}
