\section{Introduction}
\label{sec:intro}

Person recognition 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 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 fitting.  

In this paper we show that skeleton fitting is accurate and unique enough in
individuals to be used for person recognition.  We make the following
contributions.  First, we show that ground truth skeleton measurements can
uniquely identify a person.  Second, we evaluate our hypothesis using
real-world data collected with the Kinect.  Our results show that skeleton
recognition performs quite well, particularly in situations where face
recognition cannot be performed.
%As the resolution and accuracy of range cameras improve, so will the accuracy
%and precision of skeleton fitting algorithms.  

Much of the prior work in person recognition focuses on data gathered from
other sensors, such as face recognition with color images and voice
recognition with microphones.  In the realm of depth imaging, most of the work
surrounds behavioral recognition, continuing work in gait recognition.  

The paper is organized as follows.  First we discuss prior methods of
person recognition, in addition to the advances in the technologies
pertaining to skeleton fitting (Section~\ref{sec:related}).  Next we
use a dataset of actual skeletal measurements to show that
recognition by skeleton is feasible
(Section~\ref{sec:uniqueness}). We then discuss an error model and the
resulting algorithm to do person recognition
(Section~\ref{sec:algorithms}).  Finally, we collect skeleton data with
a Kinect in an uncontrolled setting and we apply preprocessing and
classification algorithms to this dataset
(Section~\ref{sec:experiment}).  We evaluate the performance of
skeleton recognition with varying group size and compare it to face
recognition. We conclude by discussing challenges working with the
Kinect and future work (Section~\ref{sec:conclusion}).

