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@@ -1,11 +1,12 @@ \section{Introduction} \label{sec:intro} -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 -skeletal measurements as a new physiological biometric for recognition. +Person recognition has become a valuable tool, whether for means of +authentication, personalization, or other applications. Previous work in +person recognition uses either physiological biometrics, such as facial +features, or behavioral biometrics like gait analysis. In this paper, we +propose skeletal measurements as a new physiological biometric for +recognition. In recent years, advances in range cameras have given us access to increasingly accurate real-time depth imaging. Furthermore, the low-cost and widely @@ -13,26 +14,28 @@ 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 fitting. -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. 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. +%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. Second, we propose two models for +skeleton recognition. Finally, we evaluate our hypothesis using real-world +data collected with the Kinect. Our results show that skeleton recognition can +identify three people with 95\% accuracy, and five people with 85\% accuracy. +Furthermore, skeleton recognition can be performed in more situations than face +recognition, such as when a person is not facing the camera. %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 images and voice -recognition with microphones. In the realm of depth imaging, most of the work -surrounds behavioral recognition, continuing work in gait recognition. +%Much of the prior work in person recognition focuses on data gathered from +%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 paper is organized as follows. First we discuss prior methods of 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 -recognition by skeleton is feasible +skeletons are a unique enough descriptor for person recognition. (Section~\ref{sec:uniqueness}). We then discuss an error model and the resulting algorithm to do person recognition (Section~\ref{sec:algorithms}). Finally, we collect skeleton data with |