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diff --git a/conclusion.tex b/conclusion.tex index c54bc9a..40eac53 100644 --- a/conclusion.tex +++ b/conclusion.tex @@ -1,24 +1,33 @@ \section{Conclusion} \label{sec:conclusion} -In this paper, we introduce skeleton recognition. We show that skeleton -measurements are unique enough to distinguish individuals using a dataset of -real skeletons. We present a probabilistic model for recognition, and extend -it to take advantage of consecutive frames. Finally we test our model by -collecting data for a week in a real-world setting. Our results show that -skeleton recognition performs close to face recognition, and it can be -used in other scenarios. +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. -However, the Kinect SDK does have some limitations. First of all, the Kinect +%we introduce skeleton recognition. We show that skeleton +%measurements are unique enough to distinguish individuals using a dataset of +%real skeletons. We present a probabilistic model for recognition, and extend +%it to take advantage of consecutive frames. Finally we test our model by +%collecting data for a week in a real-world setting. Our results show that +%skeleton recognition performs close to face recognition, and it can be +%used in other scenarios. + +Skeleton recognition has room for improvement. First of all, the Kinect SDK can only fit two skeletons at a time. Therefore, when a group of people walk in front of the Kinect, not all of them can be recognized via skeleton, -where they might be by face recognition. Second, some times figure detection -gives false positives, which caused skeletons to be fit on a window and a -vacuum cleaner during our data collection (both of these are reflective -surfaces, which might explain the failure). +where they might be by face recognition. Second, figure detection can +give false positives, which caused skeletons to be fit on a window and a +vacuum cleaner during our data collection. -Skeleton recognition can only get more accurate as the resolution of range -cameras increases and skeleton fitting algorithms improve. Microsoft is +Finally, as the resolution of range cameras increases and skeleton fitting +algorithms improve, so will the accuracy of skeleton recognition. Microsoft is planning on putting the Kinect technology inside laptops~\footnote{\url{http://www.thedaily.com/page/2012/01/27/012712-tech-kinect-laptop/}} and the Asus Xtion |