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| -rw-r--r-- | references.bib | 36 | ||||
| -rw-r--r-- | related.tex | 112 | ||||
| -rw-r--r-- | uniqueness.tex | 1 | ||||
| -rw-r--r-- | utils.tex | 2 |
4 files changed, 122 insertions, 29 deletions
diff --git a/references.bib b/references.bib index b2a3a58..0d69384 100644 --- a/references.bib +++ b/references.bib @@ -320,3 +320,39 @@ pages = "331--342" publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
+@article{Boulgouris:2007:HGR:1231536.1231758,
+ author = {Boulgouris, Nikolaos V. and Chi, Zhiwei X.},
+ title = {Human gait recognition based on matching of body components},
+ journal = {Pattern Recogn.},
+ volume = {40},
+ issue = {6},
+ month = {June},
+ year = {2007},
+ issn = {0031-3203},
+ pages = {1763--1770},
+ numpages = {8},
+ publisher = {Elsevier Science Inc.},
+ address = {New York, NY, USA},
+}
+
+@inproceedings{Johnson:2001:MMG:646073.677462,
+ author = {Johnson, Amos Y. and Bobick, Aaron F.},
+ title = {A Multi-view Method for Gait Recognition Using Static Body Parameters},
+ booktitle = {Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication},
+ series = {AVBPA '01},
+ year = {2001},
+ isbn = {3-540-42216-1},
+ pages = {301--311},
+ numpages = {11},
+ publisher = {Springer-Verlag},
+ address = {London, UK},
+}
+
+@inproceedings{zhao20063d,
+ title={3D gait recognition using multiple cameras},
+ author={Zhao, G. and Liu, G. and Li, H. and Pietikainen, M.},
+ booktitle={Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on},
+ pages={529--534},
+ year={2006},
+ organization={IEEE}
+}
diff --git a/related.tex b/related.tex index 06ae2f9..b4d163d 100644 --- a/related.tex +++ b/related.tex @@ -1,37 +1,93 @@ \section{Related Work} \label{sec:related} -The related work for this paper is divided into two main sections: using -biometrics for identification and advances in skeleton mapping. +%The related work for this paper is divided into two main sections: using +%biometrics for identification and advances in skeleton mapping. +% +%\paragraph{Biometrics} + +%One way to view different biometrics are how intrusive they are. More +%intrusive biometrics include fingerprints, irises, and DNA; less intrusive are +%faces, speech, and gait~\cite{phillips98}. Generally, there is a tradeoff +%between the level of user cooperation required and the accuracy that can be +%attained~\cite{liu01,bio-survey}. -\paragraph{Biometrics} A biometric is a distinguishable trait that can be measured and used for -identification. There are many different possible biometrics. One way to view -different biometrics are how intrusive they are. More intrusive biometrics -include fingerprints, irises, and DNA; less intrusive are faces, speech, and -gait~\cite{phillips98}. Generally, there is a tradeoff between the level of -user cooperation required and the accuracy that can be -attained~\cite{liu01,bio-survey}. +identification. There are many different possible biometrics. +Biometrics can be divided into physiological and behavioral traits. +Physiological traits include faces, fingerprints, and irises; speech and gait +are behavioral. Faces and gait are the most relevant biometrics for this paper +as they both can be collected passively and involve image processing. + +Approaches to gait recognition fall into two categories: silhouette-based and +model-based. Silhouette-based techniques recognize gaits from a binary +representation of the silhouette as extracted from each image, while +model-based techniques fit a 3-D model to the silhouette to better track +motion~\cite{gait-survey}. Some model-based techniques use features of the +generated model to aid in gait recognition, but they are severely limited by +the difficulty in generating a 3-D model from a 2-D image~\cite{}. +Furthermore, behavioral traits typically are more characteristic as opposed to +unique, and are subject to change with time and based on the observed +activity~\cite{seven-issues}. On the other hand, face recognition, as a +physiological biometric, is a more static feature. Face recognition can be +broken down into three parts: face detection, feature extraction, and +classification; these three parts are studied both individually and +together~\cite{face-survey}. + + +In this paper, we propose using skeleton measurements as a biometric trait +separate from gait biometrics. According to Jain~\etal, a proper biometric has +the following characteristics: \first \emph{universality}, everyone has it, +\second \emph{uniqueness}, it should be different between any two people, +\third \emph{permanence}, it does not vary with time, and \fourth +\emph{collectability}, it is measurable. Universality, permanence, and +collectability are easily met with skeletons (skeletal changes that occur with +age happen gradually). Uniqueness is also met, but we discuss this in more +detail in \xref{sec:uniqueness}. + +By using skeleton as a biometric for recognition, we can formulate skeleton +recognition in a similar way as we can face recognition. The equivalent parts +would be figure detection, skeleton fitting, and classification. Figure +detection and skeleton fitting map to silhouette extraction and model fitting +in gait detection, but as previously noted, they are severely limited. +However, Zhao~\etal~\cite{zhao20063d} perform gait recognition in 3-D using +multiple cameras. By moving to 3-D, many of the problems related to silhouette +extraction and model fitting are removed. Additionally, by moving to 3-D, we +can take advantage of the wealth of research relating to motion +capture~\cite{mocap-survey}. Specifically, range cameras offer real-time depth +imaging, and the Kinect~\cite{kinect} in particular is a widely available range +camera with a low price point. Figure detection and skeleton fitting have also +been studied in motion capture, mapping to region of interest detectors and +human body part identification or pose estimation respectively in this +context~\cite{plagemann:icra10,ganapathi:cvpr10,leyvand:computer11}. +Furthermore, OpenNI~\cite{openni} and Kinect SDK~\cite{kinect-sdk} are two +commercial systems that perform figure detection and skeleton fitting for the +Kinect. Given the maturity of the solutions, we will use implementations of +figure detection and skeleton fitting. Therefore this paper will focus +primarily on the classification part of skeleton recognition. + -Alternatively, biometrics can be divided into physiological and behavioral -traits. Physiological traits include faces, fingerprints, and irises; speech -and gait are behavioral. Face and gait recognition are the most relevent -biometrics for this paper as they both can be collected passively and involve -image processing. Gait recognition usually involves determining the outline of -a person from an image to measure gait, but can also be measured from floor -sensors or wearable sensors~\cite{gait-survey}. Face recognition can be broken -down into three parts: face detection, feature extraction, and identification; -these three parts are studied both individually and together~\cite{face-survey}. -We will compare skeleton recognition to face recognition in this paper for the -following two reasons. First, behavioral traits typically are more -characteristic as opposed to unique for identification beyond a certain -scale~\cite{seven-issues}, and second, face recognition is more widely studied -and accepted as a means of accurate identification~\cite{face-survey}. +%a person from an image to measure gait, but can also be measured from floor +%sensors or wearable sensors~\cite{gait-survey}. +% +%Face recognition can be broken +%down into three parts: face detection, feature extraction, and identification; +%these three parts are studied both individually and together~\cite{face-survey}. +%We will compare skeleton recognition to face recognition in this paper for the +%following two reasons. +%First, behavioral traits typically are more +%characteristic as opposed to unique for identification beyond a certain +%scale~\cite{seven-issues}, +%and second, face recognition is more widely studied +%and accepted as a means of accurate identification~\cite{face-survey}. +% +%\paragraph{Skeleton mapping} +% +%but algorithms similar to gait +%recognition have been developed for range cameras as well~\cite{gomez:hgbu11}. +% +%also including at least one example incorporating +%a range camera~\cite{gordon91}. -\paragraph{Skeleton mapping} -but algorithms similar to gait -recognition have been developed for range cameras as well~\cite{gomez:hgbu11}. -also including at least one example incorporating -a range camera~\cite{gordon91}. diff --git a/uniqueness.tex b/uniqueness.tex index 9a89c6c..68d7d2d 100644 --- a/uniqueness.tex +++ b/uniqueness.tex @@ -1,4 +1,5 @@ \section{Skeleton uniqueness} +\label{sec:uniqueness} The most obvious concern raised by trying to use skeletons to recognize people is their uniqueness. Are skeletons consistently @@ -26,7 +26,7 @@ \newcommand{\third}{\emph{(iii)}~}
\newcommand{\fourth}{\emph{(iv)}~}
\newcommand{\fifth}{\emph{(v)}~}
-\newcommand{\sixth}{\emph{(iv)}~}
+\newcommand{\sixth}{\emph{(vi)}~}
\newcommand{\zb}{z.\,B. \@}
|