@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}},
}

@inbook{2076,
  abstract     = {{Segmentation and feature extraction algorithms based on Wavelet Transform or Wavelet Packet Transform are established in pattern recognition. Especially in the field of texture analysis they are known to be practical. One difficulty of texture analysis was in the past the characterization of different printing processes. In this paper we present a new algorithmic concept to feature extraction of textures, printed by different printing techniques, without the necessity of a previous teaching phase. The typical characters of distinct printed textures are extracted by first order statistical moments of wavelet coefficients. The algorithm uses the 2D incomplete shift invariant Wavelet Packet Transform, resulting in a fast execution time of O(<i>N</i>log<sub>2</sub>(<i>N</i>)). Since the incomplete shift invariant Wavelet Packet Transform was exclusively defined for 1D-signals, it has been modified in this research. The application describes the detection of different printed security textures. }},
  author       = {{Glock, Stefan and Gillich, Eugen and Schaede, Johannes and Lohweg, Volker}},
  booktitle    = {{Pattern Recognition}},
  editor       = {{Denzler, J. and Notni, G. and Süße, H.}},
  isbn         = {{978-3-642-03797-9}},
  keywords     = {{Discrete Wavelet Transform, Wavelet Transform, Wavelet Packet, Decomposition Level, Printing Technique}},
  pages        = {{422--431}},
  publisher    = {{Springer}},
  title        = {{{Feature Extraction Algorithm for Banknote Textures based on Incomplete Shift Invariant Wavelet Packet Transform}}},
  doi          = {{https://doi.org/10.1007/978-3-642-03798-6_43}},
  volume       = {{5748}},
  year         = {{2009}},
}