Publications by Hatef Otroshi Shahreza

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

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