@article{2104,
  abstract     = {{Maintaining confidence in  security  documents,  especially  banknotes,  is  and  remains  a  major  concern  for  the  central  banks in order to maintain the stability of the economy around the world. In this paper we describe an image processing and  pattern  recognition  approach  which  is  based  on  the  Sound-of-Intaglio  concept  [1]  for  the  usage  in  smart  devices  such  as  smartphones.  Today,  in  many  world  regions  smartphones  are  in  use.  These  devices  become  more  and  more  computing units, equipped with resource-limited but effective CPUs, cameras with illumination, and flexible operating systems.  Hence,  it  appears  to  be  obvious,  to  apply  those  smartphones  for  banknote  authentication,  especially  for  visually impaired persons. However, it has to be researched, whether those devices are capable of processing  the  data  under the constraints of image quality and processing power. Our results show that it is in general possible to use such devices for banknote authentication applications.}},
  author       = {{Lohweg, Volker and Dörksen, Helene and Gillich, Eugen and Hildebrand, Roland and Hoffmann, Jan Leif and Schaede, Johannes}},
  journal      = {{Optical Document Security - The Conference on Optical Security and Counterfeit Detection III}},
  keywords     = {{authentication, anti-counterfeit features, mobile device, smartphone, wavelet transform, pattern recognition, Sound-of-Intaglio}},
  title        = {{{Mobile Devices for Banknote Authentication – is it possible? In: Optical Document Security - The Conference on Optical Security and Counterfeit Detection III, San Francisco, CA, USA, January 18-20, 2012. }}},
  year         = {{2012}},
}

@inproceedings{2116,
  abstract     = {{Favored by hardware development, since the mid-2000s, cameras can be found in mobile phones. With the advent of the Apple iPhonethey are equipped with a multi-touch high-resolution display. Their included  battery  and  low  costs  make  them  attractive  for  smart  cameraapplications.  This  paper  shows  several  scenarios,  in  which  advantagesand disadvantages of smartphones are inspected. A real-life applicationis  given,  which  shows  that  a  phone  of  this  kind  can  be  used  for  printinspection and banknote authentication}},
  author       = {{Gillich, Eugen and Hildebrand, Roland and Hoffmann, Jan Leif and Dörksen, Helene and Lohweg, Volker}},
  booktitle    = {{BVAu 2012 - 3. Jahreskolloquium "Bildverarbeitung in der Automation" Centrum Industrial IT, Lemgo,}},
  keywords     = {{smart camera, smartphones, banknotes, authentication}},
  publisher    = {{inIT-Institut für industrielle Informationstechnik}},
  title        = {{{Smartphones as Smart Cameras – Is It Possible?}}},
  year         = {{2012}},
}

@inproceedings{2082,
  abstract     = {{A robust vision system for the counterfeit detection of bank ATM keyboards is presented. The approach is based on the continuous inspection of a keyboard surface by the authenticity verification of coded covert surface features. For the surface coding suitable visual patterns on the keyboard are selected while considering constraints from the visual imperceptibility, robustness and geometrical disturbances to be encountered from the aging effects. The system’s robustness against varying camera-keyboard distances, lighting conditions and dirt-and-scratches effects is investigated. Finally, a demonstrator working in real-time is developed in order to publicly demonstrate the surface authentication process.}},
  author       = {{Iqbal, Taswar and Le, Dinh Khoi and Nolte, Michael and Lohweg, Volker}},
  booktitle    = {{32nd Annual Conference on Artificial Intelligence Paderborn | September 15 – 18, 2009, accepted for Publication}},
  isbn         = {{978-3-642-04616-2}},
  keywords     = {{ATMs, human perception, counterfeit resistance, digital authentication, surface coding, pattern recognition}},
  pages        = {{347--354}},
  publisher    = {{Springer}},
  title        = {{{Human Perception Based Counterfeit Detection for Automated Teller Machines, KI 2009, Artificial Intelligence and Automation }}},
  doi          = {{https://doi.org/10.1007/978-3-642-04617-9_44}},
  volume       = {{5803}},
  year         = {{2009}},
}