related work redux

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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.
+%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}.
 
-\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}.
+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 trait
+separate from gait biometrics.  According to Jain~\etal, 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).  Uniqueness is also met, but we discuss this in more
+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.
+
 
-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}.
+%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}.  
 
-\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}.