MorDeephy: Face Morphing Detection via Fused Classification

Iurii Medvedev, Farhad Shadmand, Nuno Gonçalves, Nuno Gonçalves

2023

Abstract

Face morphing attack detection (MAD) is one of the most challenging tasks in the field of face recognition nowadays. In this work, we introduce a novel deep learning strategy for a single image face morphing detection, which implies the discrimination of morphed face images along with a sophisticated face recognition task in a complex classification scheme. It is directed onto learning the deep facial features, which carry information about the authenticity of these features. Our work also introduces several additional contributions: the public and easy-to-use face morphing detection benchmark and the results of our wild datasets filtering strategy. Our method, which we call MorDeephy, achieved the state of the art performance and demonstrated a prominent ability for generalizing the task of morphing detection to unseen scenarios.

Paper Citation

in Harvard Style

Medvedev I., Shadmand F. and Gonçalves N. (2023). MorDeephy: Face Morphing Detection via Fused Classification. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 193-204. DOI: 10.5220/0011606100003411

in Bibtex Style

@conference{icpram23,
author={Iurii Medvedev and Farhad Shadmand and Nuno Gonçalves},
title={MorDeephy: Face Morphing Detection via Fused Classification},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={193-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011606100003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - MorDeephy: Face Morphing Detection via Fused Classification
SN - 978-989-758-626-2
AU - Medvedev I.
AU - Shadmand F.
AU - Gonçalves N.
PY - 2023
SP - 193
EP - 204
DO - 10.5220/0011606100003411