Printed source identification by microscopic images

Min Jen Tsai, Imam Yuadi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)


The research of printed source identification is generally processed by scanned images which are limited by the scanner resolution. The accuracy of source identification is also bound by this limitation. In this study, microscopic images are used for printed source identification based on its high magnification capability for detailed texture and structure information. To explore the relationship between source printers and images obtained by the microscope, the proposed approach utilizes image processing techniques and data exploration methods to calculate many important features, i.e., Local Binary Pattern (LBP), Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filter and Haralick filter. Among different set of features, LBP approach achieves the highest identification rate which is significantly superior to other methods. Consequently, the proposed technique using microscopic images achieves high classification accuracy rate which show promising applications for real world digital forensics research.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781467399616
Publication statusPublished - 3 Aug 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States
Duration: 25 Sept 201628 Sept 2016

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States


  • Forensics
  • Local Binary Pattern (LBP)
  • Microscopic Images
  • Printed document


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