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Face morphing attack detection (MAD) is one of the most challenging tasks inthe field of face recognition nowadays. In this work, we introduce a novel deeplearning strategy for a single image face morphing detection, which implies thediscrimination of morphed face images along with a sophisticated facerecognition task in a complex classification scheme. It is directed ontolearning the deep facial features, which carry information about theauthenticity of these features. Our work also introduces several additionalcontributions: the public and easy-to-use face morphing detection benchmark andthe results of our wild datasets filtering strategy. Our method, which we callMorDeephy, achieved the state of the art performance and demonstrated aprominent ability for generalising the task of morphing detection to unseenscenarios.

MorDeephy: Face Morphing Detection Via Fused Classification