Type of Publication

Conference Papers

Date:

2 /

2023

Status

Published

DOI:

10.5220/0011606100003411

MorDeephy: Face Morphing Detection via Fused Classification

Featured in:

12th International Conference on Pattern Recognition Application and Methods, Lisbon, Portugal.

Authors:

Iurii Medvedev, Farhad Shadmand and Nuno Gonçalves

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.

Citation
Iurii Medvedev, Farhad Shadmand and Nuno Gonçalves. “MorDeephy: Face morphing detection via fused classification.” 12th International Conference on Pattern Recognition Application and Methods (ICPRAM). Lisbon, Portugal (2023). DOI: 10.5220/0011606100003411.

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