From f69c178b85958ef0773a97e5946cce722639415e Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Mon, 5 Mar 2012 13:47:41 -0800
Subject: last changes

---
 conclusion.tex | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

(limited to 'conclusion.tex')

diff --git a/conclusion.tex b/conclusion.tex
index 40eac53..d423e29 100644
--- a/conclusion.tex
+++ b/conclusion.tex
@@ -1,15 +1,15 @@
 \section{Conclusion}
 \label{sec:conclusion}
 
-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.
+In this paper, we present exciting and promising results for skeleton
+recognition.  With greater than 90\% accuracy for less than 10 people, skeleton
+recognition can already be used in households, \eg to load personalized
+settings for 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 it is dark.
 
 %we introduce skeleton recognition.  We show that skeleton
 %measurements are unique enough to distinguish individuals using a dataset of
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