In this paper, we create mix-and-matched generative networks to address privacy and bias
concerns in face recognition systems. There has been a rise in bias based on religion, gender, and race. To preserve the robustness of face ID systems while masking these bias-inducing facial features, we map the faces to neutral natural landscape images. This still leaves the possibility of estimating facial features from the landscape images. We address this issue through decorrelation shuffling functions between the latent spaces of the encoder and the generator networks, as a way of decorrelating facial and landscape features and preventing hacking.
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