Facial Demorphing via Identity Preserving Image Decomposition

TL;DR

This paper introduces a novel reference-free facial demorphing technique that accurately decomposes a face morph into its original identities, enhancing security in face recognition systems.

Contribution

It presents a new method treating demorphing as an ill-posed decomposition problem, capable of recovering bonafide faces without needing reference images.

Findings

High-quality reconstruction of original identities.

Effective on multiple datasets including CASIA-WebFace, SMDD, AMSL.

Outperforms existing reference-based demorphing methods.

Abstract

A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby undermining the security of a face recognition system. To deal with this problem, several morph attack detection techniques have been developed. But these methods do not extract any information about the underlying bonafides used to create them. Demorphing addresses this limitation. However, current demorphing techniques are mostly reference-based, i.e, they need an image of one of the identities to recover the other. In this work, we treat demorphing as an ill-posed decomposition problem. We propose a novel method that is reference-free and recovers the bonafides with high accuracy. Our method decomposes the morph into several identity-preserving feature components. A merger network then weighs and combines these components to recover the bonafides. Our method is observed to reconstruct high-quality bonafides in terms of definition and fidelity. Experiments on the CASIA-WebFace, SMDD and AMSL datasets demonstrate the effectiveness of our method.