Detection For Non-Genuine Identification Documents

Abstract

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.