Publications associées à Spoofing Face Recognition with 3D Masks

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Touradj Ebrahimi, Lin Yuan, Xiao Pu, Hongbo Li, Yao Zhang

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

Ieee-Inst Electrical Electronics Engineers Inc2024

The Current State of the OBI DICT Project: A Bilingual e-Dictionary of Oracle-Bone Inscriptions with AI Image Recognition

This article reports on the current state of the OBI DICT project, a bilingual e-dictionary of oracle-bone inscriptions (OBI), incorporating artificial intelligence (AI) image recognition technology. It first provides a brief overview of the development of ...

Buro Van Die Wat2024

Recognizing distant faces

Marin Vogelsang, Lukas Vogelsang

As an 'early alerting' sense, one of the primary tasks for the human visual system is to recognize distant objects. In the specific context of facial identification, this ecologically important task has received surprisingly little attention. Most studies ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Explanation of Face Recognition via Saliency Maps

Touradj Ebrahimi, Yuhang Lu

Despite the significant progress in recent years, deep face recognition is often treated as a "black box" and has been criticized for lacking explainability. It becomes increasingly important to understand the characteristics and decisions of deep face rec ...

2023

Discriminative Deep Feature Visualization for Explainable Face Recognition

Touradj Ebrahimi, Yuhang Lu, Zewei Xu

Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their ``black-box'' nature. In recent years, studies have been carried out to give an interp ...

2023

Towards Visual Saliency Explanations of Face Verification

Touradj Ebrahimi, Yuhang Lu, Zewei Xu

In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. Despite the high accuracy, they are often criticized for lacking explainability. The ...

2024

Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity Scores

Daniel Gatica-Perez, Lakmal Buddika Meegahapola

Machine learning models trained with passive sensor data from mobile devices can be used to perform various inferences pertaining to activity recognition, context awareness, and health and well-being. Prior work has improved inference performance through t ...

Assoc Computing Machinery2023

How to Boost Face Recognition with StyleGAN?

Nikita Durasov

State-of-the-art face recognition systems require vast amounts of labeled training data. Given the priority of privacy in face recognition applications, the data is limited to celebrity web crawls, which have issues such as limited numbers of identities. O ...

Ieee Computer Soc2023

Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UK

Daniel Gatica-Perez, Karim Assi, Lakmal Buddika Meegahapola

Smartphones enable understanding human behavior with activity recognition to support peoples daily lives. Prior studies focused on using inertial sensors to detect simple activities (sitting, walking, running, etc.) and were mostly conducted in homogeneous ...

Assoc Computing Machinery2023

Regularization of polynomial networks for image recognition

Volkan Cevher, Bohan Wang, Grigorios Chrysos

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

2023

Regularization of polynomial networks for image recognition

Volkan Cevher, Bohan Wang, Grigorios Chrysos

Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Polynomial Networks (PNs) have emerged as an alternative method with a promising ...

Ieee Computer Soc2023

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Pranav Mamidanna, Mackenzie Mathis, Alexander Mathis, Kai Jappe Sandbrink, Matthias Bethge

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

eLIFE SCIENCES PUBL LTD2023

Automated post-earthquake damage assessment of stone masonry buildings integrating machine learning, computer vision, and physics-based modeling

Bryan German Pantoja Rosero

Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...

EPFL2023

Are GAN-based Morphs Threatening Face Recognition?

Sébastien Marcel, Eklavya Sarkar, Laurent Colbois

Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control ...

IEEE2022

Indexing Protected Deep Face Templates by Frequent Binary Patterns

Sébastien Marcel, Hatef Otroshi Shahreza

In this work, we present a simple biometric indexing scheme which is binning and retrieving cancelable deep face templates based on frequent binary patterns. The simplicity of the proposed approach makes it applicable to unprotected as well as protected, i ...

IEEE2022

A Novel Framework for Assessment of Deep Face Recognition Systems in Realistic Conditions

Touradj Ebrahimi, Luca Barras, Yuhang Lu

Deep convolutional neural networks have shown remarkable results on face recognition (FR). Despite their significant progress, the performance of current face recognition techniques is often assessed in benchmarks under not always realistic conditions. The ...

2022

Natural-Looking Adversarial Examples From Freehand Sketches

Davide Nanni, Hakgu Kim

Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to misl ...

IEEE2022

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Sébastien Marcel, Hatef Otroshi Shahreza

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of ...

IEEE2022

A Two-Step Approach To Leverage Contextual Data: Speech Recognition In Air-Traffic Communications

Petr Motlicek, Amrutha Prasad, Juan Pablo Zuluaga Gomez

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead ...

IEEE2022