Publications by Hatef Otroshi Shahreza

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

Alexander Carl Unnervik, Hatef Otroshi Shahreza, Parsa Rahimi Noshanagh

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, Hatef Otroshi Shahreza, Parsa Rahimi Noshanagh

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

Knowledge Distillation for Face Recognition Using Synthetic Data With Dynamic Latent Sampling

Hatef Otroshi Shahreza

State-of-the-art face recognition models are computationally expensive for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets, raising privacy and ethical concerns. Generating synthetic datasets ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Breaking Template Protection: Reconstruction of Face Images from Protected Facial Templates

Hatef Otroshi Shahreza

Face recognition systems tend toward ubiquity and are commonly utilized for security purposes. These systems operate based on facial representations, called templates, extracted by a deep neural network from each face image. However, it has been shown that ...

IEEE2024

EdgeFace: Efficient Face Recognition Model for Edge Devices

Hatef Otroshi Shahreza

In this paper, we present EdgeFace - a lightweight and efficient face recognition network inspired by the hybrid architecture of EdgeNeXt. By effectively combining the strengths of both CNN and Transformer models, and a low rank linear layer, EdgeFace achi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Template Inversion Attack Using Synthetic Face Images Against Real Face Recognition Systems

Hatef Otroshi Shahreza

In this paper, we use synthetic data and propose a new method for template inversion attacks against face recognition systems. We use synthetic data to train a face reconstruction model to generate high-resolution (i.e., 1024 x 1024) face images from facia ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

Face Recognition Using Lensless Camera

Hatef Otroshi Shahreza

Coded aperture imaging is an emerging technique allowing thin form factor cameras that can be cheaply constructed. Many applications benefit from using such lensless cameras, such as face recognition. We propose a method for face recognition using coded ap ...

IEEE2024

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

Vulnerability of State-of-the-Art Face Recognition Models to Template Inversion Attack

Hatef Otroshi Shahreza

Face recognition systems use the templates (extracted from users' face images) stored in the system's database for recognition. In a template inversion attack, the adversary gains access to the stored templates and tries to enter the system using images re ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Face Reconstruction from Partially Leaked Facial Embeddings

Hatef Otroshi Shahreza

Face recognition systems are widely used in different applications. In such systems, some features (called templates) are extracted from each face image and stored in the system's database. In this paper, we propose an attack against face recognition syste ...

IEEE2024

On the Information in Deep Biometric Templates: from Vulnerability of Unprotected Templates to Leakage in Protected Templates

Hatef Otroshi Shahreza

Biometric recognition systems tend toward ubiquity and are widely being used in different applications for authentication purposes. Compared to conventional authentication tools, such as PIN or password, which are always in danger of being forgotten or sto ...

EPFL2024

On Measuring Linkability of Multiple Protected Biometric Templates Using Maximal Leakage

Hatef Otroshi Shahreza, Yanina Yurina Shkel

With the rapid development of biometric recognition systems, users can be simultaneously enrolled in multiple biometric recognition systems, either with a single or multiple biometric characteristics (e.g., face, voice, etc.). With such a growth of biometr ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2024

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

Alexander Carl Unnervik, Hatef Otroshi Shahreza, Parsa Rahimi Noshanagh

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

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Sébastien Marcel, Hatef Otroshi Shahreza

This study focuses on the protection of soft-biometric attributes related to the demographic information of individuals that can be extracted from compact representations of face images, called embeddings. We consider a state-ofthe-art technology for soft- ...

IEEE COMPUTER SOC2023

SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

Hatef Otroshi Shahreza

State-of-the-art face recognition networks are often computationally expensive and cannot be used for mobile applications. Training lightweight face recognition models also requires large identity-labeled datasets. Meanwhile, there are privacy and ethical ...

IEEE2023

Blackbox Face Reconstruction from Deep Facial Embeddings Using A Different Face Recognition Model

Hatef Otroshi Shahreza

Face recognition systems generally store features (called embeddings) extracted from each face image during the enrollment stage, and then compare the extracted embeddings with the stored embeddings during the recognition stage. In this paper, we focus on ...

New York2023

Inversion of Deep Facial Templates using Synthetic Data

Hatef Otroshi Shahreza

In this paper, we use synthetic data and propose a new method to reconstruct high-resolution face images from facial templates in a template inversion attack against face recognition systems. We use a pre-trained face generator network to generate syntheti ...

IEEE2023

Approximating Optimal Morphing Attacks using Template Inversion

Hatef Otroshi Shahreza

Recent works have demonstrated the feasibility of inverting face recognition systems, enabling to recover convincing face images using only their embeddings. We leverage such template inversion models to develop a novel type of deep morphing attack based o ...

IEEE2023

Template Inversion Attack against Face Recognition Systems using 3D Face Reconstruction

Hatef Otroshi Shahreza

Face recognition systems are increasingly being used in different applications. In such systems, some features (also known as embeddings or templates) are extracted from each face image. Then, the extracted templates are stored in the system's database dur ...

Ieee Computer Soc2023

Measuring Linkability of Protected Biometric Templates Using Maximal Leakage

Hatef Otroshi Shahreza, Yanina Yurina Shkel

As the applications of biometric recognition systems are increasing rapidly, there is a growing need to secure the sensitive data used within these systems. Considering privacy challenges in such systems, different biometric template protection (BTP) schem ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023