Fused Classification For Differential Face Morphing Detection
Iurii Medvedev Joana Pimenta Nuno Gonçalves
Face morphing, a sophisticated presentation attack technique, poses
significant security risks to face recognition systems. Traditional methods
struggle to detect morphing attacks, which involve blending multiple face
images to create a synthetic image that can match different individuals. In
this paper, we focus on the differential detection of face morphing and propose
an extended approach based on fused classification method for no-reference
scenario. We introduce a public face morphing detection benchmark for the
differential scenario and utilize a specific data mining technique to enhance
the performance of our approach. Experimental results demonstrate the
effectiveness of our method in detecting morphing attacks.