jon's pass part 2

1 files changed, 23 insertions, 14 deletions

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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 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.
+In this paper, we present exciting and promising results for face recognition.
+With greater than 90\% accuracy for less than 10 people, skeleton recognition
+can already be used in households, \eg to load personalized settings on a home
+entertainment system.  Skeleton recognition performs less than 10\% worse than
+face recognition in the current setting.  This is a good result considering
+face recognition has been studied for years and is more mature.  Furthermore,
+skeleton recognition works in many situations when face recognition does not.
+For example, when a person is not facing the camera or when there is not enough
+light.
 
-However, the Kinect SDK does have some limitations.  First of all, the Kinect
+%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.
+
+Skeleton recognition has room for improvement. 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).
+where they might be by face recognition.  Second, figure detection can
+give false positives, which caused skeletons to be fit on a window and a
+vacuum cleaner during our data collection.
 
-Skeleton recognition can only get more accurate as the resolution of range
-cameras increases and skeleton fitting algorithms improve.  Microsoft is
+Finally,  as the resolution of range cameras increases and skeleton fitting
+algorithms improve, so will the accuracy of skeleton recognition.  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