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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Juvenile Morph Dataset: A Study of Attack Detectability and Recognition Vulnerability
A morph is an image of an ambiguous subject generated by combining multiple individuals. The morphed image can be submitted to a facial recognition system and erroneously verified with the contributing bad actors. When submitted as a passport image, a morphed face poses a national security threat because a passport can then be shared between the individuals. As morphed images become easier to generate, it is vital that the research community expands available datasets in order to contentiously improve current technology. Children are a challenging paradigm for facial recognition systems and morphing children takes advantage of this disparity. In this paper, we morph juvenile faces in order to create a unique, high-quality dataset to challenge FRS. To the best of our knowledge, this is the first study on the generation and evaluation of juvenile morphed faces. The evaluation of the generated morphed juvenile dataset is performed in terms of vulnerability analysis and presentation attack error rates.
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- Award ID(s):
- 1650474
- NSF-PAR ID:
- 10401289
- Date Published:
- Journal Name:
- uvenile Morph Dataset: A Study of Attack Detectability and Recognition Vulnerability
- Page Range / eLocation ID:
- 1 to 5
- 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/HST56032.2022.10025448
