meer content

content/introduction.tex

@@ -14,13 +14,4 @@
 % 
 % Here we have the typical use of a "T" for an initial drop letter
 % and "HIS" in caps to complete the first word.
-\IEEEPARstart{T}{his} demo file is intended to serve as a ``starter file''
-for IEEE journal papers produced under \LaTeX\ using
-IEEEtran.cls version 1.8b and later.
-% You must have at least 2 lines in the paragraph with the drop letter
-% (should never be an issue)
-I wish you the best of success.
-
-\hfill mds
- 
-\hfill August 26, 2015
+\IEEEPARstart{F}{or} the assignment of Introduction to Biometrics an analysis needs to be done on Verilook and the Face REcognition Grand Challenge (FRGC) database. Where Verilook is state of the art face recognition software and the FRGC database consists of 50,000 recordings. The Verilook system will generate matching scores between two pictures from the FRGC database. The verification performance is usually abstracted from a single ROC. It is possible though to test this performance on a individual ROC\rq{}s. Which gives the possibility to find cases and/or individuals for which the verification performance drops.

content/method.tex

@@ -1,6 +1,6 @@
 \section{Method}
 \subsection{Selecting pictures from the database}
-The FRGC dataset consist more then…. pictures of more then …. different persons. Since it is not practical to analysis all pictures from the database.  The amount of pictures available per individual is not constant. Therefor only a section of the dataset will be analyzed.
+The FRGC dataset consist more then 50,000 pictures of more then 4000 different persons. Since it is not practical to analysis all pictures from the database.  The amount of pictures available per individual is not constant. Therefor only a section of the dataset will be analyzed.
 
 Since it is interesting to know why some individuals have better recognizing performance then others there should be no different in the circumstance the picture is taken. Therefore, only pictures taken in a controlled light environment are taken form the dataset. 
 
@@ -24,6 +24,6 @@ Verilook is not a stable program, after a random amount of comparisons the Ncore
 \subsection{Get the ROC from every subject}
 Calculation all the ROC\rq{}s will be done with the use of MATLAB. All the comparison scores are stored in a upper triangular matrix. First step is to sort all the comparison scores per subject and per picture. To store this information two structs where generated. The picture struct contains all genuine and imposter scores, the amount of failed matches for the picture is stored as \lq\lq{}noface\rq\rq{} The subject struct contains all genuine and imposter scores as well as all the picture scores of this particular subject, these picture scores contain the same information as the picture struct.
 
-With the use of a loop the true and false match ratings are calculated for each subject. The loop tests for each threshold value between zero and the maximum matching score how many impostor and genuine scores there are. Both the impostor score \[s>t\] and genuine score \[s>t\] is divided by the total amount of impostor and genuine comparisons. The outcome of each threshold is stored in an array which is stored in the subject struct. To generate the overall ROC all these true and false match ratings are summed and divided by the total amount of subjects.
+With the use of a loop the true and false match ratings are calculated for each subject. The loop tests for each threshold value between zero and the maximum matching score how many impostor and genuine scores there are. Both the impostor score $s>t$ and genuine score $s>t$ is divided by the total amount of impostor and genuine comparisons. The outcome of each threshold is stored in an array which is stored in the subject struct. To generate the overall ROC all these true and false match ratings are summed and divided by the total amount of subjects.
 
-To test the system it is important to visualize all the scores and images. Therefor a MATLAB function is build which will generate \latex script. This document combines per subject all pictures, ROC and scores. The overall scores per subject are represented in a table with the maximum, minimum, mean and standard deviation of the impostor and genuine scores. For further analysis the individual score per picture is added. This enables a human to test what differences in pictures reduce or increase the change on a correct match.
+To test the system it is important to visualize all the scores and images. Therefor a MATLAB function is build which will generate \LaTeX script. This document combines per subject all pictures, ROC and scores. The overall scores per subject are represented in a table with the maximum, minimum, mean and standard deviation of the impostor and genuine scores. For further analysis the individual score per picture is added. This enables a human to test what differences in pictures reduce or increase the change on a correct match.

content/results.tex

@@ -1,4 +1,61 @@
 \section{results}
 
 \subsection{Overall ROC}
-\subsection{Individual ROC’s}
+The overall ROC can be found in Figure \ref{fig:overallROC}. With a false acceptance rate of 0,05, The recognition performance is above 99,88\%
+\begin{figure}[ht]
+\centering
+\includegraphics[width=.8\linewidth]{image/final_ROC_log}
+\caption{ The overall ROC}
+\label{fig:overallROC}
+\end{figure}
+
+\subsection{Individual ROC’s}
+18 out of the 31 individuals have a perfect ROC. Which means that the highest impostor score is lower than the lowest genuine score. An example of a perfect ROC can be found in Figure \ref{fig:nd1S04934_ROC}. All images from these 18 individuals are analyzed. In all of these images the person looks direct into the camera.  A part of the eyes is visible and no parts of the face are covered. All images have a relative high resolution of 1704~by~2272 pixels. 
+\begin{figure}[ht]
+\centering
+\includegraphics[width=.8\linewidth]{image/nd1S04934_ROC}
+\caption{A perfect ROC}
+\label{fig:nd1S04934_ROC}
+\end{figure}
+Not all ROC’s are perfect. In Figure \ref{fig:nd1S04341_ROC} is the ROC of subject nd1S04341. This deviation is caused by one matching score of zero. In figure \ref{fig:nd1S04341} are the corresponding pictures. The main difference between this pictures is the visibility of the ears. 
+\begin{figure}[ht]
+\centering
+\includegraphics[width=.8\linewidth]{image/nd1S04341_ROC}
+\caption{ROC of subject nd1S04341}
+\label{fig:nd1S04341_ROC}
+\end{figure}
+\begin{figure}[ht]
+\begin{minipage}[th]{.45\linewidth}
+\centering
+\vspace{0pt}
+\includegraphics[width=\linewidth]{image/04341d147}
+\end{minipage}\hfill
+\begin{minipage}[th]{.45\linewidth}
+\centering
+\vspace{0pt}
+\includegraphics[width=\linewidth]{image/04341d154}
+\end{minipage}\hfill
+\caption{Pictures of subject nd1S04341}
+\label{fig:nd1S04341}
+\end{figure}
+Another interesting result is the visibility of the face. In Figure 6 is the ROC of subject nd1S04406. There are a few genuine which a relative low. One of these matching scores in the matching score between the pictures in Figure 6. There are a few differences between these pictures. The first is the visibility of the left part of the face. In the left picture a bigger part of the face is covered with hair. Furthermore, only in the left picture, the teeth are shown.
+\begin{figure}[ht]
+\centering
+\includegraphics[width=.8\linewidth]{image/nd1S04406_ROC}
+\caption{ROC of subject nd1S04406}
+\label{fig:nd1S04406_ROC}
+\end{figure}
+\begin{figure}[ht]
+\begin{minipage}[th]{.45\linewidth}
+\centering
+\vspace{0pt}
+\includegraphics[width=\linewidth]{image/04341d147}
+\end{minipage}\hfill
+\begin{minipage}[th]{.45\linewidth}
+\centering
+\vspace{0pt}
+\includegraphics[width=\linewidth]{image/04341d154}
+\end{minipage}\hfill
+\caption{Pictures of subject nd1S04341}
+\label{fig:nd1S04341}
+\end{figure}

introduction-to-biometrics-paper.tex

@@ -109,11 +109,11 @@
 
 
 
-
+\usepackage{graphicx}
 % *** GRAPHICS RELATED PACKAGES ***
 %
 \ifCLASSINFOpdf
-  % \usepackage[pdftex]{graphicx}
+
   % declare the path(s) where your graphic files are
   % \graphicspath{{../pdf/}{../jpeg/}}
   % and their extensions so you won't have to specify these with