MorDeephy: Face Morphing Detection via Fused Classification

• Main Author: Medvedev, Iurii
• Publication Date: 2023
• Other Authors: Shadmand, Farhad, Gonçalves, Nuno
• Format: Article
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: https://hdl.handle.net/10316/115039; https://doi.org/10.5220/0011606100003411
• topic: 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.