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\section{Introduction}
\label{sec:intro}
Person recognition has become a valuable tool, whether for means of
authentication, personalization, or other applications. Previous work in
person recognition uses either physiological biometrics, such as facial
features, or behavioral biometrics like gait analysis. In this paper, we
propose 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, 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 propose two models for
skeleton recognition. Finally, we evaluate our hypothesis using real-world
data collected with a Kinect. Our results show that skeleton recognition can
identify three people with 95\% accuracy, and five people with 85\% accuracy.
Furthermore, skeleton recognition can be performed in situations where face
recognition cannot, such as when a person is not facing the camera.
%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 skeletons are a unique
enough descriptor for person recognition. (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. We apply preprocessing and
classification algorithms to this dataset and evaluate the performance of
skeleton recognition with varying group size (Section~\ref{sec:experiment}).
We conclude by discussing challenges working with the Kinect and future work
(Section~\ref{sec:conclusion}).
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