Fused Classification for Differential Face Morphing Detection

Face morphing, a sophisticated presentation attack tech- nique, poses signi cant security risks to face recognition systems. Traditional methods struggle to detect morph- ing attacks, which involve blending multiple face images to create a synthetic image that can match different individ- uals. In this paper, we focus on the differential detection of face morphing and propose an extended approach based on fused classi cation 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.