Residual Colour Scale-Space Gradients for Reference-based Face Morphing Attack Detection

Raghavendra Ramachandra, Guoqiang Li

Abstract: Face biometrics has become an integral part of the various security and law enforcement applications, including border control scenarios. However, the face recognition systems are vulnerable to the morphing attacks, and thus, it is essential to develop a reliable and robust face Morphing Attack Detection (MAD) techniques. This paper presents a novel approach based on the residual gradients computed from the face image's colour scale-space representation in the reference-based or differential set-up. Thus, the proposed method will take two facial images (one from the passport and another from the trusted device) to compute the residual gradients, which is then classified using Spectral Regression Kernel Discriminant Analysis (SRKDA) to reliable detect the face morphing attacks. Extensive experiments are carried out on two different datasets to benchmark the performance of the proposed method, especially to different morph generation methods, morphing data mediums (digital, print-scan and print-scan compression) and ageing variations. Experimental results demonstrate the improved performance of the proposed method over the state-of-the-art reference-based face MAD in all evaluation protocols.