brano's comments

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 \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 an 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
+many more 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.