Detection For Non-Genuine Identification Documents

Calvin Yap, Koksheik Wong, Ganesh Krishnasamy, Ian K. T. Tan

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Due to the recent pandemic, electronic know-your-customer (e-KYC) technologies have enabled business continuity for various sectors. In the case of Malaysia, the Malaysia National Identity card (MyKad) is often used as the official document for identity-verification purposes. However, when presenting MyKad online, the uploaded MyKad could be an image of the actual MyKad taken a few years back, a printout of a legit MyKad, or a digitally/physically tampered MyKad. This research aims to detect non-genuine MyKad. Specifi-cally, we consider the unique colour count, the surface rough-ness score (i.e., complexity), and local binary patterns (LBP) of the received MyKad image to make inferences. The proposed method achieves an accuracy of 97.71% and an Fl-score of 0.9773.
Original languageEnglish
Title of host publication2022 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)
ISBN (Electronic)9798350332421
Publication statusPublished - 31 Mar 2023


  • Tampering detection
  • e-KYC
  • local binary patterns
  • surface roughness
  • unique colour count

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing


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