\section{Conclusion}
\label{sec:conclusion}

In this paper, we introduce skeleton recognition.  We show that skeleton
measurements are unique enough to distinguish individuals using a dataset of
real skeletons.  We present a probabilistic model for recognition, and extend
it to take advantage of consecutive frames. Finally we test our model by
collecting data for a week in a real-world setting.  Our results show that
skeleton recognition performs close to face recognition, and it can be
used in other scenarios.

However, the Kinect SDK does have some limitations.  First of all, the Kinect
SDK can only fit two skeletons at a time.  Therefore, when a group of people
walk in front of the Kinect, not all of them can be recognized via skeleton,
where they might be by face recognition.  Second, some times figure detection
gives false positives, which caused skeletons to be fit on a window and a
vacuum cleaner during our data collection (both of these are reflective
surfaces, which might explain the failure).

Skeleton recognition can only get more accurate as the resolution of range
cameras increases and skeleton fitting algorithms improve.  Microsoft is
planning on putting the Kinect technology inside
laptops~\footnote{\url{http://www.thedaily.com/page/2012/01/27/012712-tech-kinect-laptop/}}
and the Asus Xtion
pro~\footnote{\url{http://www.asus.com/Multimedia/Motion_Sensor/Xtion_PRO/}} is
a range camera like the Kinect designed for PCs. The increased usage of range
cameras and competition among vendors can only lead to advancements in the
associated technologies. 
