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\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 can be collected passively and involve
image processing. Gait recognition usually involves determining the outline of
a person from an image to measure gait, but can also be measured from floor
sensors or wearable sensors~\cite{gait-survey}. Face recognition can be broken
down into three parts: face detection, feature extraction, and identification;
these three parts are studied both individually and together~\cite{face-survey}.
We will compare skeleton recognition to face recognition in this paper for the
following two reasons. First, behavioral traits typically are more
characteristic as opposed to unique for identification beyond a certain
scale~\cite{seven-issues}, and second, face recognition is more widely studied
and accepted as a means of accurate identification~\cite{face-survey}.
\paragraph{Skeleton mapping}
but algorithms similar to gait
recognition have been developed for range cameras as well~\cite{gomez:hgbu11}.
also including at least one example incorporating
a range camera~\cite{gordon91}.
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