face file

5 files changed, 1141 insertions, 33 deletions

diff --git a/data/combined/graphs/face.csv b/data/combined/graphs/face.csv
new file mode 100644
index 0000000..3b0284b
--- /dev/null
+++ b/data/combined/graphs/face.csv
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diff --git a/data/combined/scriptSkeleton.m b/data/combined/scriptSkeleton.m
index 91c724f..0dfbfa0 100644
--- a/data/combined/scriptSkeleton.m
+++ b/data/combined/scriptSkeleton.m
@@ -69,6 +69,8 @@ recall = zeros(numEx, 0);
 for ex = 1 : numEx

   train = trains{ex};

   test = tests{ex};

+  length(train)

+  length(test)

   

   % NB classifier with multivariate Gaussians

   Py = zeros(numClasses, 1);

@@ -116,6 +118,8 @@ for ex = 1 : numEx
   

   % prediction

   [conf, yp] = max(logp, [], 2);

+  size(yp)

+  exit

   

   % sum up all but the highest probability

   norm1 = logp - repmat(conf, 1, numClasses);

diff --git a/data/face-frame-recognition-accuracy.py b/data/face-frame-recognition-accuracy.py
index 0d34cf9..3ff0aa8 100755
--- a/data/face-frame-recognition-accuracy.py
+++ b/data/face-frame-recognition-accuracy.py
@@ -1,4 +1,5 @@
 #!/usr/bin/python
+import numpy as np
 import os
 import sys
 import pickle
@@ -22,8 +23,8 @@ for line in open(sys.argv[1]):
     except:
         continue
     user = line[1]
-    if run not in runs:
-        runs[run] = 0
+    if run not in labels:
+        #runs[run] = 0
         labels[run] = users.index(user) + 1
 
 for line in open(sys.argv[2]):
@@ -41,23 +42,28 @@ for line in open(sys.argv[2]):
     conf[run] = float(line[7])
     prun = run
 
-t=0.0
-tp=0.0
-fp=0.0
-fn=0.0
-for (k,v) in runs.items():
-    #print v,labels[k]
-    if v == 0 or conf[k] < thresh:
-        fn += 1
-    elif v != labels[k]:
-        fp += 1
-    else:
-        tp += 1
-    t += 1
-#print runs[167],labels[167]
-#print tp,fp,fn
-#print("Precision: ",tp/(tp+fp))
-#print("False positives: ",fp/(tp+fp))
-#print("Recall: ",1.0-fn/t)
-print str(tp/(tp+fp))+","+str(1.0-fn/t)
+for i in range(999)+list(np.arange(999,1000,0.01)):
+    thresh = i/10
+    t=0.0
+    tp=0.0
+    fp=0.0
+    fn=0.0
+    for (k,v) in runs.items():
+        #print v,labels[k]
+        if conf[k] < thresh:
+            fn += 1
+        elif v != labels[k]:
+            fp += 1
+        else:
+            tp += 1
+        t += 1
+    #print runs[167],labels[167]
+    #print tp,fp,fn
+    #print("Precision: ",tp/(tp+fp))
+    #print("False positives: ",fp/(tp+fp))
+    #print("Recall: ",1.0-fn/t)
+    try:
+        print str(1.0-fn/t)+","+str(tp/(tp+fp))
+    except:
+        print "0,1"
 
diff --git a/experimental.tex b/experimental.tex
index 388e628..be1fc16 100644
--- a/experimental.tex
+++ b/experimental.tex
@@ -32,7 +32,7 @@ frame.  For each frame where a person is detected and a skeleton is fitted we
 collect the 3D coordinates of 20 body joints, and the color image recorded by
 the RGB camera.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.99\textwidth]{graphics/hallway.png}
   \end{center}
@@ -71,12 +71,11 @@ Head-ShoulderCenter, ShoulderCenter-Shoulder, Shoulder-Elbow, Elbow-Wrist,
 ShoulderCenter-Spine, Spine-HipCenter, HipCenter-HipSide, HipSide-Knee,
 Knee-Ankle.  Finally, any frame with a missing feature is filtered out.
 
-Each detected skeleton also has an ID number which identifies which figure
-it maps to from the figure detection stage. When there are consecutive number stays the
-same across several frames, it means that the skeleton-fitting
-algorithm was able to detect the skeleton in a contiguous way. This
-allows us to define the concept of a \emph{run}: a sequence of frames
-with the same skeleton ID.
+Each detected skeleton also has an ID number which identifies the figure it
+maps to from the figure detection stage. When there are consecutive frames with
+the same ID, it means that the skeleton-fitting algorithm was able to detect
+the skeleton in a contiguous way. This allows us to define the concept of a
+\emph{run}: a sequence of frames with the same skeleton ID.
 
 \begin{table}
 \begin{center}
@@ -120,7 +119,7 @@ number of runs available for the least present people, as seen in
 Table~\ref{tab:dataset}, which does not permit a proper training of
 the algorithm.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/10fold-naive.pdf}
   \end{center}
@@ -151,7 +150,7 @@ subsample. Figure~\ref{fig:sequential} shows the prediction-recall
 curve when averaging the prediction rate of the 10 incremental
 experiments.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/online-sht.pdf}
   \end{center}
@@ -195,7 +194,7 @@ classification and some of runs are dropped because it is not possible
 to recognize the person. Apart from that, the data set reduction is
 performed exactly as explained in Section~\ref{sec:experiment-design}.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/back.pdf}
   \end{center}
@@ -244,7 +243,7 @@ Figure~\ref{fig:var} compares the Precision-Recall curve of
 Figure~\ref{fig:sequential} to the curve of the same experiment run on
 the newly obtained data set.
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=0.80\textwidth]{graphics/var.pdf}
   \end{center}
diff --git a/uniqueness.tex b/uniqueness.tex
index fc53bab..854e4c2 100644
--- a/uniqueness.tex
+++ b/uniqueness.tex
@@ -66,7 +66,7 @@ defined as:
   \end{cases}
 \end{displaymath}
 
-\begin{figure}
+\begin{figure}[t]
   \begin{center}
     \includegraphics[width=10cm]{graphics/roc.pdf}
   \end{center}