last changes

1 files changed, 23 insertions, 17 deletions

diff --git a/experimental.tex b/experimental.tex
index d8ea62c..d03763b 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -285,15 +285,21 @@ recognition, but not by a large margin.
 
 We use the publicly available REST API of \textsf{face.com} to do face
 recognition on our dataset.  Due to the restrictions of the API, for this
-experiment we train on one half of the data and test on the remaining half. For
-comparison, the MoG algorithm is run with the same training-testing partitioning of
-the dataset. In this setting, SHT is not relevant for the comparison, because
-\textsf{face.com} does not give the possibility to mark a sequence of frames as
-belonging to the same run. This additional information would be used by the SHT
-algorithm and would thus bias the results in favor of skeleton recognition.
+experiment we set $n_p = 5$ and train on one half of the data and test on the
+remaining half. For comparison, the MoG algorithm is run with the same
+training-testing partitioning of the dataset. In this setting, SHT is not
+relevant for the comparison, because \textsf{face.com} does not give the
+possibility to mark a sequence of frames as belonging to the same run. This
+additional information would be used by the SHT algorithm and would thus bias
+the experiment in favor of skeleton recognition.  The results are shown in
+\fref{fig:face}.  Face recognition outperforms skeleton recognition, but by
+less than 10\% at most thresholds. 
+%These results are promising, given that
+%\textsf{face.com} is the state-of-the-art in face recognition.
+
 %However, this result does not take into account the disparity in the number of
-%runs which face recognition and skeleton recognition can classify frames, which
-%we discuss in the next experiment.
+%runs which face recognition and skeleton recognition can classify frames,
+%which we discuss in the next experiment.
 
 
 
@@ -327,15 +333,15 @@ algorithm and would thus bias the results in favor of skeleton recognition.
 In the next experiment, we include the runs in which people are walking away
 from the Kinect that we could positively identify.  The performance of face
 recognition outperforms skeleton recognition in the previous setting.  However,
-there are many cases where only skeleton recognition is possible. The most
-obvious one is when people are walking away from the camera. Coming back to the
-raw data collected during the experiment design, we manually label the runs of
-people walking away from the camera. In this case, it is harder to get the
-ground truth classification and some of runs are dropped because it is not
-possible to recognize the person. Apart from that, the dataset reduction is
-performed exactly as explained in Section~\ref{sec:experiment-design}. Our
-results show that we can identify people walking away from the camera just as
-well as when they are walking towards the camera.
+there are many cases where only skeleton recognition is possible. For example,
+when people are walking away from the Kinect. Coming back to the raw data
+collected during the experiment design, we manually label the runs of people
+walking away from the camera. In this case, it is harder to get the ground
+truth classification and some of runs are dropped because it is not possible to
+recognize the person. Apart from that, the dataset reduction is performed
+exactly as explained in Section~\ref{sec:experiment-design}. Our results show
+that we can identify people walking away from the camera comparably to when
+they are walking towards the camera.
 
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