From b072ca61a5bf1b36fb6eaa06232bfc5472db4dbf Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Tue, 28 Feb 2012 02:42:44 -0800
Subject: first part of related work

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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 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}
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