From 2fa61c47c9e93fdc4c4908dd9ee6e7885430e73b Mon Sep 17 00:00:00 2001
From: Jon Whiteaker <jbw@berkeley.edu>
Date: Sun, 4 Mar 2012 00:51:57 -0800
Subject: brano's comments

---
 intro.tex | 38 +++++++++++++++++---------------------
 1 file changed, 17 insertions(+), 21 deletions(-)

(limited to 'intro.tex')

diff --git a/intro.tex b/intro.tex
index 5e51a36..bd643e2 100644
--- a/intro.tex
+++ b/intro.tex
@@ -1,7 +1,7 @@
 \section{Introduction}
 \label{sec:intro}
 
-Person identification has become a valuable asset, whether for means of
+Person recognition has become a valuable asset, whether for means of
 authentication, personalization, or other applications.  Previous work revolves
 around either physiological biometrics, such as face recognition, or behavioral
 biometrics such as gait recognition.  In this paper, we propose using
@@ -11,35 +11,31 @@ In recent years, advances in range cameras have given us access to increasingly
 accurate real-time depth imaging.  Furthermore, the low-cost and widely
 available Kinect~\cite{kinect} has brought range imaging to the masses.  In
 parallel, the automatic detection of body parts from depth images has led to
-real-time skeleton mapping.  As the resolution and accuracy of range cameras
-improve, so will the accuracy and precision of skeleton mapping algorithms.  
+real-time skeleton fitting.  
 
-In this paper we show that skeleton mapping is accurate and unique enough in
-individuals to be used for person recognition.  We make the following two
+In this paper we show that skeleton fitting is accurate and unique enough in
+individuals to be used for person recognition.  We make the following
 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.
+uniquely identify a person.  Second, we evaluate our hypothesis using
+real-world data collected with the Kinect.  Our results show that skeleton
+recognition performs quite well, particularly in situations where face
+recognition cannot be performed.
+%As the resolution and accuracy of range cameras improve, so will the accuracy
+%and precision of skeleton fitting algorithms.  
 
 Much of the prior work in person recognition focuses on data gathered from
-other sensors, such as face recognition with color cameras and voice
+other sensors, such as face recognition with color images and voice
 recognition with microphones.  In the realm of depth imaging, most of the work
-surrounds behavioral recognition, continuing work in gait recognition.  The
-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.
+surrounds behavioral recognition, continuing work in gait 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
+person recognition, in addition to the advances in the technologies
+pertaining to skeleton fitting (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
+recognition by skeleton is feasible
 (Section~\ref{sec:uniqueness}). We then discuss an error model and the
-resulting algorithm to do person identification
-(Section~\ref{sec:algorithm}).  Finally, we collect skeleton data with
+resulting algorithm to do person recognition
+(Section~\ref{sec:algorithms}).  Finally, we collect skeleton data with
 a Kinect in an uncontrolled setting and we apply preprocessing and
 classification algorithms to this dataset
 (Section~\ref{sec:experiment}).  We evaluate the performance of
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