Joint Learning of Assignment and Representation for Biometric Group Membership

02/24/2020
by   Marzieh Gheisari, et al.
0

This paper proposes a framework for group membership protocols preventing the curious but honest server from reconstructing the enrolled biometric signatures and inferring the identity of querying clients. This framework learns the embedding parameters, group representations and assignments simultaneously. Experiments show the trade-off between security/privacy and verification/identification performances.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/10/2018

Aggregation and Embedding for Group Membership Verification

This paper proposes a group membership verification protocol preventing ...
research
04/23/2019

Privacy Preserving Group Membership Verification and Identification

When convoking privacy, group membership verification checks if a biomet...
research
02/24/2020

Group Membership Verification with Privacy: Sparse or Dense?

Group membership verification checks if a biometric trait corresponds to...
research
06/17/2022

AggNet: Learning to Aggregate Faces for Group Membership Verification

In some face recognition applications, we are interested to verify wheth...
research
12/31/2017

Confidence set for group membership

This paper develops procedures for computing a confidence set for a late...
research
03/10/2023

Distributionally Robust Optimization with Probabilistic Group

Modern machine learning models may be susceptible to learning spurious c...
research
09/16/2015

Group Membership Prediction

The group membership prediction (GMP) problem involves predicting whethe...

Please sign up or login with your details

Forgot password? Click here to reset