Fused Classification For Differential Face Morphing Detection

TL;DR

This paper introduces a fused classification approach for differential face morphing detection, addressing security risks in face recognition systems by improving detection accuracy without reference images.

Contribution

It presents a novel fused classification method for no-reference face morphing detection and introduces a new benchmark dataset for differential scenarios.

Findings

Effective detection of morphing attacks demonstrated

Benchmark dataset enables standardized evaluation

Enhanced performance with data mining techniques

Abstract

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.