Publications by Alexander Carl Unnervik

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

SDFR: Synthetic Data for Face Recognition Competition

Alexander Carl Unnervik, Hatef Otroshi Shahreza

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic ...

IEEE2024

Second Edition Frcsyn Challenge at Cvpr 2024: Face Recognition Challenge in the Era of Synthetic Data

Alexander Carl Unnervik

Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases p ...

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

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Alexander Carl Unnervik

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

EPFL2024

An anomaly detection approach for backdoored neural networks: face recognition as a case study

Sébastien Marcel, Alexander Carl Unnervik

Backdoor attacks allow an attacker to embed functionality jeopardizing proper behavior of any algorithm, machine learning or not. This hidden functionality can remain inactive for normal use of the algorithm until activated by the attacker. Given how steal ...

IEEE2022