Autor(es): Medvedev, Iurii ; Shadmand, Farhad ; Gonçalves, Nuno
Data: 2023
Identificador Persistente: https://hdl.handle.net/10316/115039
Origem: Estudo Geral - Universidade de Coimbra
Assunto(s): Face Morphing Detection; Face Recognition; Deep Learning; Convolutional Neural Networks; Classification
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.
Portuguese Mint and Official Printing Office (INCM) and the Institute of Systems and Robotics-the University of Coimbra - project Facing.
