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| author | Thibaut Horel <thibaut.horel@gmail.com> | 2012-03-05 14:34:54 -0800 |
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| committer | Thibaut Horel <thibaut.horel@gmail.com> | 2012-03-05 14:34:54 -0800 |
| commit | 898a85e9d27cac39f403a9a499ea49578a856f4f (patch) | |
| tree | 2501910f1d4aec0123804d0fe5ab76714758f00f /uniqueness.tex | |
| parent | 75bdb4858889f2af6e074ed9448b6ded1a81cbc4 (diff) | |
| download | kinect-898a85e9d27cac39f403a9a499ea49578a856f4f.tar.gz | |
Final changes
Diffstat (limited to 'uniqueness.tex')
| -rw-r--r-- | uniqueness.tex | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/uniqueness.tex b/uniqueness.tex index 6c90310..1814446 100644 --- a/uniqueness.tex +++ b/uniqueness.tex @@ -83,10 +83,10 @@ as well as the ROC of the best performing face detection algorithm in the image-restricted LFW benchmark: \emph{Associate-Predict} \cite{associate}. -The results show that with a standard deviation of 3mm, nearest +The results show that with a standard deviation of 3 millimeters, nearest neighbor performs quite similarly to face detection at low false-positive rate. At this noise level, the error is smaller than -1cm with 99.9\% probability. Even with a standard deviation of 5mm, it +1 centimeter with 99.9\% probability. Even with a standard deviation of 5 millimeters, it is still possible to detect 90\% of the matched pairs with a false positive rate of 6\%. |