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 morphed 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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Identical Twins Face Morph Database Generation
By combining two or more face images of look-alikes,
morphed face images are generated to fool Facial Recognition Systems (FRS) into falsely accepting multiple people, leading to failures in security systems. Despite several attempts in the literature, finding pairs of bona fide
faces to generate the morphed images is still a challenging problem. In this paper, we morph identical twin pairs
to generate extremely difficult morphs for FRS. We first
explore three methods of morphed face generation, GAN-based, landmark-based, and a wavelet-based morphing approach. We leverage these methods to generate morphs
from the identical twin pairs that retain high similarity to
both subjects while resulting in minimal artifacts in the visual domain. To further improve the difficulty of recognizing
morphed face images, we perform an ablation study to apply adversarial perturbation to the morphs such that they
cannot be detected by trained morph classifiers. The evaluation of the generated identical twin-morphed dataset is
performed in terms of vulnerability analysis and presentation attack error rates.
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- Award ID(s):
- 1650474
- PAR ID:
- 10401290
- Date Published:
- Journal Name:
- Identical Twins Face Morph Database Generation
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IJCB54206.2022.10007973
