Publications by Parsa Rahimi Noshanagh

Second Frcsyn-ongoing: Winning Solutions and Post-challenge Analysis to Improve Face Recognition with Synthetic Data

Alexander Carl Unnervik, Parsa Rahimi Noshanagh, Hatef Otroshi Shahreza

Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also of ...

ELSEVIER2025

FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems

Alexander Carl Unnervik, Parsa Rahimi Noshanagh, Hatef Otroshi Shahreza

This article presents FRCSyn-onGoing, an ongoing challenge for face recognition where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases and standard experimental protoc ...

Elsevier2024

Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition

Parsa Rahimi Noshanagh

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 framework. We rigorously address the trade-off between obfuscation and utility. Bo ...

IEEE2024

FRCSyn Challenge atWACV 2024: Face Recognition Challenge in the Era of Synthetic Data

Alexander Carl Unnervik, Parsa Rahimi Noshanagh, Hatef Otroshi Shahreza

Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail. This paper offers an overview of the Face Recogni ...

IEEE2024

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Sébastien Marcel, Parsa Rahimi Noshanagh, Christophe René Joseph Ecabert

Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various facial attributes such as gender and origin. The main reason are the biases in ...

IEEE2023