Facial Demorphing via Identity Preserving Image Decomposition
Nitish Shukla, Arun Ross

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
