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Fused Classification for Differential Face Morphing Detection - VisTeam

Type of Publication

Conference Papers

Date:

1 /

2024

Status

Published

DOI:

10.1109/WACVW60836.2024.00114

Fused Classification for Differential Face Morphing Detection

Featured in:

IEEE/CVF Winter Conference on Applications of Computer Vision 2024

Authors:

Iurii Medvedev, Joana Alves Pimenta and Nuno Gonçalves

Abstract

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.

Citation
Iurii Medvedev, Joana Alves Pimenta and Nuno Gonçalves (2024). Fused Classification For Differential Face Morphing Detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 1043-1050). DOI: 10.1109/WACVW60836.2024.00114

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