V-MAD: Video-based Morphing Attack Detection in Operational Scenarios
Guido Borghi Annalisa Franco Nicolò Di Domenico Matteo Ferrara Davide Maltoni
In response to the rising threat of the face morphing attack, this paper
introduces and explores the potential of Video-based Morphing Attack Detection
(V-MAD) systems in real-world operational scenarios. While current morphing
attack detection methods primarily focus on a single or a pair of images, V-MAD
is based on video sequences, exploiting the video streams often acquired by
face verification tools available, for instance, at airport gates. Through this
study, we show for the first time the advantages that the availability of
multiple probe frames can bring to the morphing attack detection task,
especially in scenarios where the quality of probe images is varied and might
be affected, for instance, by pose or illumination variations. Experimental
results on a real operational database demonstrate that video sequences
represent valuable information for increasing the robustness and performance of
morphing attack detection systems.