Face Anonymization
6 papers with code • 2 benchmarks • 3 datasets
LFW
FDF
FDH
Most implemented papers
DeepPrivacy: A Generative Adversarial Network for Face Anonymization
Our model is based on a conditional generative adversarial network, generating images considering the original pose and image background.
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
In many real-world scenarios like people tracking or action recognition, it is important to be able to process the data while taking careful consideration in protecting people's identity.
DeepPrivacy2: Towards Realistic Full-Body Anonymization
Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures.
GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions
The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face.
Does Image Anonymization Impact Computer Vision Training?
Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization.
DisguisOR: Holistic Face Anonymization for the Operating Room
wngtn/disguisor
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Methods: RGB and depth images from multiple cameras are fused into a 3D point cloud representation of the scene.
