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 \section{Introduction}
 \label{sec:intro}
+
+Person identification has become a valuable asset, whether for means of
+authentication, personalization, or other applications.  Previous work revolves
+around either physiological biometrics, such as face recognition, or behavioral
+biometrics such as voice or gait recognition.  In this paper, we propose using
+skeletal measurements as a new physiological biometric for recognition.
+
+In recent years, advances in range cameras have given us access to increasingly
+accurate real-time depth imaging.  Furthermore, the low-cost and widely
+available Kinect~\cite{kinect} has brought range imaging to the masses.  In
+parallel, the automatic detection of body parts from depth images has led to
+real-time skeleton mapping.  As the resolution and accuracy of range cameras
+improve, so will the accuracy and precision of skeleton mapping algorithms.  
+
+In this paper we show that skeleton mapping is accurate and unique enough in
+individuals to be used for person recognition.  First, we show that ground
+truth skeleton measurements can uniquely identify a person.  Second, we show
+how the accuracy of skeleton recognition decreases as simulated error
+increases.  Third, we collect skeleton data with a Kinect in an uncontrolled
+setting and we apply preprocessing and classification algorithms to this
+dataset.  We evaluate the performance of skeleton recognition with varying
+group size and compare it to face recognition.
+
+Much of the prior work in person recognition focuses on data gathered from
+other sensors, such as face recognition with color cameras and voice
+recognition with microphones.  In the realm of depth imaging, most of the work
+surrounds behavioral recognition, continuing work in gait recognition.  The
+Xbox 360~\cite{} does use the height inferred from the Kinect as part of its
+user identification algorithm, albeit in addition to other attributes including
+face recognition.
+