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| -rw-r--r-- | experimental.tex | 4 | ||||
| -rw-r--r-- | related.tex | 4 |
2 files changed, 4 insertions, 4 deletions
diff --git a/experimental.tex b/experimental.tex index a7871eb..dd5a73c 100644 --- a/experimental.tex +++ b/experimental.tex @@ -176,7 +176,7 @@ varying group size $n_p = \{3,5,10,25\}$. \fref{fig:offline} shows the precision-recall plot as the threshold varies. -Both algrithms perform three times better than the majority class baseline of +Both algorithms perform three times better than the majority class baseline of 15\% with a recall of 100\% on all people. We make two main observations. First, as expected, SHT performs better than MoG because of temporal smoothing. Second, performance is inversely proportional to group size. As we test @@ -364,7 +364,7 @@ given the relatively low resolution of the Kinect's infrared camera. \fref{fig:var} compares the precision-recall curve of \fref{fig:offline:sht} to the curve of the same experiment run on the newly obtained dataset. We observe -a roughly 20\% increase in performace across most thresholds. We +a roughly 20\% increase in performance across most thresholds. We believe these results would significantly outperform face recognition in a similar setting. diff --git a/related.tex b/related.tex index ff5a763..a575c97 100644 --- a/related.tex +++ b/related.tex @@ -19,7 +19,7 @@ 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 typicaly fall into two categories: silhouette-based and +Approaches to gait recognition typically 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 @@ -55,7 +55,7 @@ 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 we can take -advantage of the wealth of research relating to 3-D motion +advantage of the wealth of research relating to \mbox{3-D} motion capture~\cite{mocap-survey}. %Specifically, range cameras offer real-time depth %imaging, and |