332 lines
14 KiB
TeX
332 lines
14 KiB
TeX
\documentclass[a0paper,landscape,final]{baposter}
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\usepackage{times}
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\usepackage{amsmath}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%% Some math symbols used in the text
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% Format
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\newcommand{\Matrix}[1]{\begin{bmatrix} #1 \end{bmatrix}}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Begin of Document
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{document}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Here starts the poster
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%%%---------------------------------------------------------------------------
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%%% Format it to your taste with the options
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\typeout{Poster Starts}
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\background{
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\begin{tikzpicture}[remember picture,overlay]%
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\draw (current page.north west)+(-2em,-0em) node[anchor=north west] {\hspace{-2em}\includegraphics[height=1.1\textheight]{silhouettes_background}};
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% Show grid to help with alignment
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grid=false,
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% Column spacing
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colspacing=1em,
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% Color style
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borderColor=reddishyellow,
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headerColorOne=yellow,
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headerColorTwo=reddishyellow,
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boxColorOne=lightyellow,
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boxColorTwo=lighteryellow,
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% Format of textbox
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textborder=roundedleft,
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eyecatcher=false,
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headerheight=0.08\textheight,
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headershape=roundedright,
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headershade=plain,
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headerfont=\Large\textsf, %Sans Serif
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boxshade=plain,
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% background=shade-tb,
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background=plain,
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linewidth=2pt
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}
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% Eye Catcher
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{} % No eye catcher for this poster. If an eye catcher is present, the title is centered between eye-catcher and logo.
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% Title
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{\sf %Sans Serif
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%\bf% Serif
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Expression Invariant Face Recognition using a 3D Morphable Model}
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% Authors
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{\sf %Sans Serif
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% Serif
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Brian Amberg\hspace{3em}
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brian.amberg@unibas.ch\hspace{3em}
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University of Basel, Switzerland
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}
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% University logo
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{{\begin{minipage}{16em}
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\hfill
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\includegraphics[height=2em]{msrlogo}
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\includegraphics[height=5.5em]{logo}
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\end{minipage}}
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}
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\tikzstyle{light shaded}=[top color=baposterBGtwo!30!white,bottom color=baposterBGone!30!white,shading=axis,shading angle=30]
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% Width of left inset image
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\newlength{\leftimgwidth}
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\setlength{\leftimgwidth}{0.78em+8.0em}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Now define the boxes that make up the poster
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%%%---------------------------------------------------------------------------
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%%% Each box has a name and can be placed absolutely or relatively.
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%%% The only inconvenience is that you can only specify a relative position
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%%% towards an already declared box. So if you have a box attached to the
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%%% bottom, one to the top and a third one which should be in between, you
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%%% have to specify the top and bottom boxes before you specify the middle
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%%% box.
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%
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% A coloured circle useful as a bullet with an adjustably strong filling
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\newcommand{\colouredcircle}[1]{%
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\tikz{\useasboundingbox (-0.2em,-0.32em) rectangle(0.2em,0.32em); \draw[draw=black,fill=baposterBGone!80!black!#1!white,line width=0.03em] (0,0) circle(0.18em);}}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Contribution}{name=contribution,column=0,row=0}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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{}We introduce a method for expression invariant face recognition. A
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generative 3D Morphable Model (3DMM) is used to separate identity and
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expression components. The expression removal results in greatly increased
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recognition performance, even on difficult datasets, without a decrease in
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performance on expression-less datasets.
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It is applicable to any kind of input data, and was evaluated here on
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textureless range scans.
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Model}{name=model,column=0,below=contribution}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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The Model was learnt from 175 subjects. We used one neutral expression scan
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per identity and 50 expression scans of a subset of the subjects.
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The identity model is a linear model build from the neutral scans.
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\begin{align}
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\VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n\qquad.
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\end{align}
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For each of the 50 expression scans, we calculated an expression vector as
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the difference between the expression scan and the corresponding neutral
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scan of that subject. This data is already mode-centered, if we regard the
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neutral expression as the natural mode of expression data. From these offset
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vectors an additional expression matrix $\MAT M_e$ was calculated, such that the complete linear Model is
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\begin{align}
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\VEC f&=\VEC\mu + \MAT M_n\VEC\alpha_n + \MAT M_e\VEC\alpha_e
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\end{align}
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The assumption here is, that the face and expression space are linearly
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independent, such that each face is represented by a unique set of
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coefficients.
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Fitting}{name=fitting,column=0,below=model}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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A Robust Nonrigid ICP method was used to fit the model to the data.
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Robustness was achieved by iteratively reweighting the correspondences and
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using hard compatability test for the closest points.
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Fitting was initialized by a simple nose detector and proceeded fully
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automatic.
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Distance Measure}{name=measure,column=0,below=fitting,above=bottom}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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The Mahalanobis angle between the identity coefficients $\VEC{\alpha_{n}}$
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was used for classification.
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Expression Neutralization}{name=results neutralization,column=1,row=0}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
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\includegraphics[height=0.42\linewidth]{16_1_tgt}&
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\includegraphics[height=0.42\linewidth]{16_1_expression}&
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\includegraphics[height=0.42\linewidth]{16_1_neutral}\\[-0.8em]
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\smaller a) Target & \smaller b) Fit & \smaller c) Normalized\\[0.8em]
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\includegraphics[height=0.42\linewidth]{16_6_tgt}&
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\includegraphics[height=0.42\linewidth]{16_6_expression}&
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\includegraphics[height=0.42\linewidth]{16_6_neutral}\\[-0.8em]
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\smaller a) Target & \smaller b) Fit & \smaller c) Normalized
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\end{tabular}
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Expression normalisation for two scans of the same individual.
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The robust fitting gives a good estimate (b) of the true face surface given
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the noisy measurement (a). It fills in holes and removes artifacts using
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prior knowledge from the face model. The pose and expression normalized faces
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(c) are used for face recognition.
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Robustness}{name=robustness,column=1,below=results neutralization,span=1,above=bottom}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}}
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\includegraphics[height=0.42\linewidth]{56_4_tgt}&
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\includegraphics[height=0.42\linewidth]{23_2_tgt}&
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\includegraphics[height=0.42\linewidth]{5_6_tgt}\\[-0.8em]
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& \smaller a) Targets & \\[0.8em]
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\includegraphics[height=0.42\linewidth]{56_4_expression}&
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\includegraphics[height=0.42\linewidth]{23_2_expression}&
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\includegraphics[height=0.42\linewidth]{5_6_expression}\\[-0.8em]
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& \smaller b) Fits &
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\end{tabular}
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The reconstruction (b) is robust against scans (a) with artifacts, noise, and
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holes.
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Results}{name=results,column=2,span=2,row=0}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\begin{multicols}{2}
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The method was evaluated on the GavabDB expression dataset which
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contains 427 Scans, with 3 neutral scans and 4 expression scans per ID.
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To test the impact of expression invariance on neutral data we used the
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UND Dataset from the Face Recognition Great Vendor Test, which contains
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953 neutral scans with one to eight scans per subject.
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\end{multicols}\vspace{-1em}
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\mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
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\begin{tabular}{cc}
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\hspace{0.5em}\scalebox{0.74}{\input{shrec_MNCG}} &
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\hspace{0.5em}\scalebox{0.74}{\input{und_MNCG}}
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\end{tabular}\\
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% \begin{multicols}{2}
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{Expression neutralization improves results on the expression dataset
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without decreasing the accuracy on the neutral testset. Plotted is the
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ratio of correct answers to the number of possible correct answers.
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%Note the different scales for the two graphs.
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%Our approach has a high accuracy on the neutral (UND) dataset.
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}
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% \end{multicols}\vspace{-1em}
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\\\mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
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\begin{tabular}{cc}
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\hspace{0.5em}\scalebox{0.74}{\input{shrec_PR}} &
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\hspace{0.5em}\scalebox{0.74}{\input{und_PR}}
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\end{tabular}\\
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% \begin{multicols}{2}
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{Plotted are precision and recall for different retrieval depths. The lower
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precision of the UND database is due to the fact that some queries have no
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correct answers.}
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% \end{multicols}\vspace{-1em}
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\\\mbox{\hspace{0.3\linewidth}\rule{0.4\linewidth}{1pt}\hspace{0.3\linewidth}}\\
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\begin{tabular}{cc}
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\hspace{0.5em}\scalebox{0.74}{\input{shrec_FARFRR}} &
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\hspace{0.5em}\scalebox{0.74}{\input{und_FARFRR}}
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\end{tabular}\\
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% \begin{multicols}{2}
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{Impostor detection is reliable, as the minimum distance to a match
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is smaller than the minimum distance to a nonmatch. }
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% \end{multicols}
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\\
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}%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Open Questions}{name=questions,column=2,span=1,above=bottom,below=results}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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While the expression and identity space are linearly independent, there is
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some expression left in the identity model. This is because a ``neutral''
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face is interpreted differently by the subjects. We investigate the
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possibilty to build an identity/expression separated model without using
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the data labelling, based on a measure of independence.
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}%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{Funding}{name=funding,column=3,span=1,above=bottom}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\smaller
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This work was supported in part by Microsoft Research through the European PhD Scholarship Programme.
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}%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\headerbox{References}{name=references,column=3,above=funding,below=results}{
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\smaller
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\vspace{-0.4em}
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\bibliographystyle{ieee}
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\renewcommand{\section}[2]{\vskip 0.05em}
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\begin{thebibliography}{1}\itemsep=-0.01em
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\setlength{\baselineskip}{0.4em}
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\bibitem{amberg07:nonrigid}
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B.~Amberg, S.~Romdhani, T. Vetter.
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\newblock {O}ptimal {S}tep {N}onrigid {ICP} {A}lgorithms for {S}urface {R}egistration
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\newblock In {\em CVPR 2007}
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\bibitem{amberg08:recognition}
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B.~Amberg, R.~Knothe, T. Vetter.
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\newblock Expression Invariant Face Recognition with a 3D Morphable Model
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\newblock In {\em AFGR 2008}
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\end{thebibliography}
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}%
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\end{poster}%
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%
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\end{document}
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