Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/115039
Title: | MorDeephy: Face Morphing Detection via Fused Classification | Authors: | Medvedev, Iurii Shadmand, Farhad Gonçalves, Nuno |
Keywords: | Face Morphing Detection; Face Recognition; Deep Learning; Convolutional Neural Networks; Classification | Issue Date: | 2023 | Publisher: | Science and Technology Publications, Lda | Project: | UIDB/00048/2020 | Serial title, monograph or event: | International Conference on Pattern Recognition Applications and Methods | 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. | URI: | https://hdl.handle.net/10316/115039 | DOI: | 10.5220/0011606100003411 | Rights: | openAccess |
Appears in Collections: | I&D ISR - Artigos em Revistas Internacionais |
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File | Description | Size | Format | |
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MorDeephy Face Morphing Detection via Fused Classification_arXiv.pdf | 4.45 MB | Adobe PDF | View/Open |
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