Landmark Enforcement and Style Manipulation for Generative Morphing
- URL: http://arxiv.org/abs/2210.10182v1
- Date: Tue, 18 Oct 2022 22:10:25 GMT
- Title: Landmark Enforcement and Style Manipulation for Generative Morphing
- Authors: Samuel Price, Sobhan Soleymani, Nasser M. Nasrabadi
- Abstract summary: We propose a novel StyleGAN morph generation technique by introducing a landmark enforcement method to resolve this issue.
Exploration of the latent space of our model is conducted using Principal Component Analysis (PCA) to accentuate the effect of both the bona fide faces on the morphed latent representation.
To improve high frequency reconstruction in the morphs, we study the train-ability of the noise input for the StyleGAN2 model.
- Score: 24.428843425522107
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Morph images threaten Facial Recognition Systems (FRS) by presenting as
multiple individuals, allowing an adversary to swap identities with another
subject. Morph generation using generative adversarial networks (GANs) results
in high-quality morphs unaffected by the spatial artifacts caused by
landmark-based methods, but there is an apparent loss in identity with standard
GAN-based morphing methods. In this paper, we propose a novel StyleGAN morph
generation technique by introducing a landmark enforcement method to resolve
this issue. Considering this method, we aim to enforce the landmarks of the
morph image to represent the spatial average of the landmarks of the bona fide
faces and subsequently the morph images to inherit the geometric identity of
both bona fide faces. Exploration of the latent space of our model is conducted
using Principal Component Analysis (PCA) to accentuate the effect of both the
bona fide faces on the morphed latent representation and address the identity
loss issue with latent domain averaging. Additionally, to improve high
frequency reconstruction in the morphs, we study the train-ability of the noise
input for the StyleGAN2 model.
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