V-MAD: Video-based Morphing Attack Detection in Operational Scenarios
This addresses the threat of face morphing attacks in security applications like airport gates, but it is incremental as it extends existing detection methods from images to videos.
This paper tackles the problem of face morphing attacks by introducing Video-based Morphing Attack Detection (V-MAD), which uses video sequences instead of single images to improve detection. Experimental results on a real operational database show that video sequences increase robustness and performance in varied quality scenarios.
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.