Facial De-morphing: Extracting Component Faces from a Single Morph

TL;DR

This paper introduces a novel generative adversarial network that can recover both original face images from a single morphed face without reference images, advancing de-morphing capabilities for biometric security.

Contribution

It presents the first method capable of de-morphing both identities simultaneously from a single image without prior information or reference images.

Findings

High visual realism of recovered images

Effective on landmark-based and generative model-based morphs

Demonstrates promising biometric similarity

Abstract

A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple identities. Current morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. The task of deducing the individual face images from a morphed face image is known as \textit{de-morphing}. Existing work in de-morphing assume the availability of a reference image pertaining to one identity in order to recover the image of the accomplice - i.e., the other identity. In this work, we propose a novel de-morphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process. We propose a generative adversarial network that achieves single image-based de-morphing with a surprisingly high degree of visual realism and biometric similarity with the original face images. We demonstrate the performance of our method on landmark-based morphs and generative model-based morphs with promising results.