\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}

%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}.

A biometric is a distinguishable trait that can be measured and used for
identification.  There are many different possible biometrics.  
Biometrics can be divided into physiological and behavioral traits.
Physiological traits include faces, fingerprints, and irises; speech and gait
are behavioral.  Faces and gait are the most relevant biometrics for this paper
as they both can be collected passively and involve image processing.

Approaches to gait recognition fall into two categories: silhouette-based and
model-based.  Silhouette-based techniques recognize gaits from a binary
representation of the silhouette as extracted from each image, while
model-based techniques fit a 3-D model to the silhouette to better track
motion~\cite{gait-survey}. Some model-based techniques use features of the
generated model to aid in gait recognition, but they are severely limited by
the difficulty in generating a 3-D model from a 2-D image~\cite{}.
Furthermore, behavioral traits typically are more characteristic as opposed to
unique, and are subject to change with time and based on the observed
activity~\cite{seven-issues}.  

On the other hand, face recognition, as a physiological biometric, is a more
static feature.  Face recognition can be broken down into three parts: face
detection, feature extraction, and classification; these three parts are
studied both individually and together~\cite{face-survey}.

In this paper, we propose using skeleton measurements as a biometric separate
from gait biometrics.  According to Jain~\etal{}~\cite{bio-survey}, a proper
biometric has the following characteristics: \first \emph{universality},
everyone has it, \second \emph{uniqueness}, it should be different between any
two people, \third \emph{permanence}, it does not vary with time, and \fourth
\emph{collectability}, it is measurable. Universality, permanence, and
collectability are easily met with skeletons (skeletal changes that occur with
age happen gradually).  We discuss how uniqueness is met in detail in
\xref{sec:uniqueness}.

By using skeleton as a biometric for recognition, we can formulate skeleton
recognition in a similar way as we can face recognition.  The equivalent parts
would be figure detection, skeleton fitting, and classification. Figure
detection and skeleton fitting map to silhouette extraction and model fitting
in gait detection, but as previously noted, they are severely limited.
However, Zhao~\etal~\cite{zhao20063d} perform gait recognition in 3-D using
multiple cameras.  By moving to 3-D, many of the problems related to silhouette
extraction and model fitting are removed.  Additionally, by moving to 3-D, we
can take advantage of the wealth of research relating to motion
capture~\cite{mocap-survey}. Specifically, range cameras offer real-time depth
imaging, and the Kinect~\cite{kinect} in particular is a widely available range
camera with a low price point.  Figure detection and skeleton fitting have also
been studied in motion capture, mapping to region of interest detectors and
human body part identification or pose estimation respectively in this
context~\cite{plagemann:icra10,ganapathi:cvpr10,leyvand:computer11}.
Furthermore, OpenNI~\cite{openni} and Kinect SDK~\cite{kinect-sdk} are two
commercial systems that perform figure detection and skeleton fitting for the
Kinect.  Given the maturity of the solutions, we will use implementations of
figure detection and skeleton fitting.  Therefore this paper will focus
primarily on the classification part of skeleton recognition.

%The
%Xbox 360~\cite{} does use the height inferred from the Kinect as part of its
%user recognition algorithm, albeit in addition to other attributes including
%face recognition.

%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}.