\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}



\usepackage{float}








\begin{document}

\title{Robust Morph-Detection at Automated Border Control Gate using Deep
Decomposed 3D Shape \& Diffuse Reflectance}



\author[1]{Jag Mohan Singh }%
\affil[1]{Norwegian University of Science and Technology}%


\vspace{-1em}



  
  \date{May 25, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Face recognition is widely employed in Automated Border Control (ABC)
gates, which verify the face image on passport or electronic Machine
Readable Travel Document (eMTRD) against the captured image to confirm
the identity of the passport holder. In this paper, we present a robust
morph detection algorithm that is based on differential morph detection.
The proposed method decomposes the bona fide image captured from the ABC
gate and the digital face image extracted from the eMRTD into the
diffuse reconstructed image and a quantized normal map. The extracted
features are further used to learn a linear classifier (SVM) to detect a
morphing attack based on the assessment of differences between the bona
fide image from the ABC gate and the digital face image extracted from
the passport. Owing to the availability of multiple cameras within an
ABC gate, we extend the proposed method to fuse the classification
scores to generate the final decision on morph-attack-detection. To
validate our proposed algorithm, we create a morph attack database with
overall 588 images, where bona fide are captured in an indoor lighting
environment with a Canon DSLR Camera with one sample per subject and
correspondingly images from ABC gates. We benchmark our proposed method
with the existing state-of-the-art and can state that the new approach
significantly outperforms previous approaches in the ABC gate scenario.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

