23,576 research outputs found
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition
Reliable facial expression recognition plays a critical role in human-machine
interactions. However, most of the facial expression analysis methodologies
proposed to date pay little or no attention to the protection of a user's
privacy. In this paper, we propose a Privacy-Preserving Representation-Learning
Variational Generative Adversarial Network (PPRL-VGAN) to learn an image
representation that is explicitly disentangled from the identity information.
At the same time, this representation is discriminative from the standpoint of
facial expression recognition and generative as it allows expression-equivalent
face image synthesis. We evaluate the proposed model on two public datasets
under various threat scenarios. Quantitative and qualitative results
demonstrate that our approach strikes a balance between the preservation of
privacy and data utility. We further demonstrate that our model can be
effectively applied to other tasks such as expression morphing and image
completion
k-Same-Siamese-GAN: k-Same Algorithm with Generative Adversarial Network for Facial Image De-identification with Hyperparameter Tuning and Mixed Precision Training
For a data holder, such as a hospital or a government entity, who has a
privately held collection of personal data, in which the revealing and/or
processing of the personal identifiable data is restricted and prohibited by
law. Then, "how can we ensure the data holder does conceal the identity of each
individual in the imagery of personal data while still preserving certain
useful aspects of the data after de-identification?" becomes a challenge issue.
In this work, we propose an approach towards high-resolution facial image
de-identification, called k-Same-Siamese-GAN, which leverages the
k-Same-Anonymity mechanism, the Generative Adversarial Network, and the
hyperparameter tuning methods. Moreover, to speed up model training and reduce
memory consumption, the mixed precision training technique is also applied to
make kSS-GAN provide guarantees regarding privacy protection on close-form
identities and be trained much more efficiently as well. Finally, to validate
its applicability, the proposed work has been applied to actual datasets - RafD
and CelebA for performance testing. Besides protecting privacy of
high-resolution facial images, the proposed system is also justified for its
ability in automating parameter tuning and breaking through the limitation of
the number of adjustable parameters
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