Description
Negative Face Recognition, or NFR, is a face recognition approach that enhances the soft-biometric privacy on the template-level by representing face templates in a complementary (negative) domain. While ordinary templates characterize facial properties of an individual, negative templates describe facial properties that does not exist for this individual. This suppresses privacy-sensitive information from stored templates. Experiments are conducted on two publicly available datasets captured under controlled and uncontrolled scenarios on three privacy-sensitive attributes.
Papers Using This Method
How Effective are Generative Large Language Models in Performing Requirements Classification?2025-04-23Normalizing Flow Regression for Bayesian Inference with Offline Likelihood Evaluations2025-04-15RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation2025-03-28Automated Non-Functional Requirements Generation in Software Engineering with Large Language Models: A Comparative Study2025-03-19NFRs in Medical Imaging2024-11-14Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild2023-05-15Zero-Shot Learning for Requirements Classification: An Exploratory Study2023-02-093D Scene Creation and Rendering via Rough Meshes: A Lighting Transfer Avenue2022-11-27Towards a Better Integration of Fuzzy Matches in Neural Machine Translation through Data Augmentation2021-01-24Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition2020-02-21