MorDeephy: Face Morphing Detection Via Fused Classification

TL;DR

MorDeephy is a deep learning-based face morphing detection method that effectively distinguishes morphed images, generalizes well to unseen scenarios, and is supported by a new benchmark and dataset filtering strategy.

Contribution

Introduces MorDeephy, a novel deep learning approach for single-image face morphing detection, along with a public benchmark and dataset filtering strategy.

Findings

Achieved state-of-the-art performance in face morphing detection.

Demonstrated strong generalization to unseen scenarios.

Provided a new benchmark and dataset filtering method.

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 generalising the task of morphing detection to unseen scenarios.