From d9c41d3c4ad0e006e719892a00bdb40874830ae2 Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Wed, 22 Feb 2012 16:10:15 -0800
Subject: new combined data

---
 intro.tex | 26 +++++++++++++++++++-------
 1 file changed, 19 insertions(+), 7 deletions(-)

(limited to 'intro.tex')

diff --git a/intro.tex b/intro.tex
index 8f67ccd..c138b1d 100644
--- a/intro.tex
+++ b/intro.tex
@@ -15,13 +15,13 @@ real-time skeleton mapping.  As the resolution and accuracy of range cameras
 improve, so will the accuracy and precision of skeleton mapping algorithms.  
 
 In this paper we show that skeleton mapping is accurate and unique enough in
-individuals to be used for person recognition.  First, we show that ground
-truth skeleton measurements can uniquely identify a person.  Second, we show
-how the accuracy of skeleton recognition decreases as simulated error
-increases.  Third, we collect skeleton data with a Kinect in an uncontrolled
-setting and we apply preprocessing and classification algorithms to this
-dataset.  We evaluate the performance of skeleton recognition with varying
-group size and compare it to face recognition.
+individuals to be used for person recognition.  We make the following two
+contributions.  First, we show that ground truth skeleton measurements can
+uniquely identify a person.  We model how the accuracy of skeleton recognition
+decreases as simulated error increases, and find it is still possible to use
+for recognition.  Second, we evaluate our hypothesis using real-world data
+collected with the Kinect.  Our results show that skeleton recognition performs
+well, particularly in situations where face recognition cannot be performed.
 
 Much of the prior work in person recognition focuses on data gathered from
 other sensors, such as face recognition with color cameras and voice
@@ -31,3 +31,15 @@ Xbox 360~\cite{} does use the height inferred from the Kinect as part of its
 user identification algorithm, albeit in addition to other attributes including
 face recognition.
 
+The paper is organized as follows.  First we discuss prior methods of person
+identification, in addition to the advances in the technologies pertaining to
+skeleton mapping (Section~\ref{sec:related}).  Next we use a dataset of actual
+skeletal measurements to show that identification by skeleton is feasible, even
+when we simulate the error of measuring skeletons with a Kinect
+(Section~\ref{sec:}).  Finally, we Lastly, we collect skeleton data with a
+Kinect in an uncontrolled setting and we apply preprocessing and classification
+algorithms to this dataset (Section~\ref{sec:}).  We evaluate the performance
+of skeleton recognition with varying group size and compare it to face
+recognition. We conclude by discussing challenges working with the Kinect and
+future work (Section~\ref{sec:conclusion}).
+
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