8 papers
Damnatio Memoriae: Adversarially and Selectively Forgetting Identities in the Embedding Space of Face Recognition Models
Ünsal Öztürk, Vedrana Krivokuća Hahn, Sushil Bhattacharjee +1
A face recognition model links two images of a person recorded on separate occasions when their embedding similarity exceeds an operating threshold. We consider making chosen ident…
Quantifying Training Membership Information in the Hyperspherical Embedding Geometry of Face Recognition Models
Ünsal Öztürk, Sébastien Marcel
Face recognition models represent each face as an embedding vector on the unit hypersphere by clustering embeddings of the same identity while pushing different identities apart th…
Variational Latent Entropy Estimation Disentanglement: Controlled Attribute Leakage for Face Recognition
Ünsal Öztürk, Vedrana Krivokuća Hahn, Sushil Bhattacharjee +1
Face recognition embeddings encode identity, but they also encode other factors such as gender and ethnicity. Depending on how these factors are used by a downstream system, separa…
ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition
Parsa Rahimi, Sebastien Marcel
Synthetic data generation is increasingly used in machine learning for training and data augmentation. Yet, current strategies often rely on external foundation models or datasets,…
AugGen: Synthetic Augmentation using Diffusion Models Can Improve Recognition
Parsa Rahimi, Damien Teney, Sebastien Marcel
The increasing reliance on large-scale datasets in machine learning poses significant privacy and ethical challenges, particularly in sensitive domains such as face recognition. Sy…
Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition
Parsa Rahimi, Behrooz Razeghi, Sebastien Marcel
In this paper, we investigate the potential of image-to-image translation (I2I) techniques for transferring realism to 3D-rendered facial images in the context of Face Recognition…
Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Behrooz Razeghi, Parsa Rahimi, Sébastien Marcel
In this study, we apply the information-theoretic Privacy Funnel (PF) model to face recognition and develop a method for privacy-preserving representation learning within an end-to…
Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition
Behrooz Razeghi, Parsa Rahimi, Sébastien Marcel
In this study, we harness the information-theoretic Privacy Funnel (PF) model to develop a method for privacy-preserving representation learning using an end-to-end training framew…