Reliable Face Morphing Attack Detection in On-The-Fly Border Control Scenario with Variation in Image Resolution and Capture Distance

TL;DR

This paper introduces a novel face morphing attack detection method tailored for on-the-fly border control scenarios, demonstrating superior performance across varying image resolutions and capture distances.

Contribution

The work proposes a new Differential-MAD algorithm using spherical interpolation and deep feature fusion from six CNNs, specifically designed for real-time border control applications.

Findings

Proposed D-MAD outperforms existing methods in experiments.

Effective across different camera resolutions and distances.

Validated on a new face morphing dataset (SCFace-Morph).

Abstract

Face Recognition Systems (FRS) are vulnerable to various attacks performed directly and indirectly. Among these attacks, face morphing attacks are highly potential in deceiving automatic FRS and human observers and indicate a severe security threat, especially in the border control scenario. This work presents a face morphing attack detection, especially in the On-The-Fly (OTF) Automatic Border Control (ABC) scenario. We present a novel Differential-MAD (D-MAD) algorithm based on the spherical interpolation and hierarchical fusion of deep features computed from six different pre-trained deep Convolutional Neural Networks (CNNs). Extensive experiments are carried out on the newly generated face morphing dataset (SCFace-Morph) based on the publicly available SCFace dataset by considering the real-life scenario of Automatic Border Control (ABC) gates. Experimental protocols are designed to benchmark the proposed and state-of-the-art (SOTA) D-MAD techniques for different camera resolutions and capture distances. Obtained results have indicated the superior performance of the proposed D-MAD method compared to the existing methods.