Publication
PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation
Publications associées (33)
Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency
Jean-Philippe Thiran, Tobias Kober, Bénédicte Marie Maréchal, Jonas Richiardi
With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...
Wiley2024Bridging the gap between theoretical and practical privacy technologies for at-risk populations
With the pervasive digitalization of modern life, we benefit from efficient access to information and services. Yet, this digitalization poses severe privacy challenges, especially for special-needs individuals. Beyond being a fundamental human right, priv ...
EPFL2023Towards 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 ...
2023On the Recognition Performance of BioHashing on state-of-the-art Face Recognition models
Sébastien Marcel, Hatef Otroshi Shahreza
Face recognition has become a popular authentication tool in recent years. Modern state-of-the-art (SOTA) face recognition methods rely on deep neural networks, which extract discriminative features from face images. Although these methods have high recogn ...
IEEE2021Learning How To Recognize Faces In Heterogeneous Environments
Face recognition is a mature field in biometrics in which several systems have been proposed over the last three decades.
Such systems are extremely reliable under controlled recording conditions and it has been deployed in the field in critical tasks, suc ...
EPFL2019Privacy-Enhancing Technologies for Mobile Applications and Services
Over a third of the world's population owns a smartphone. As generic computing devices that support a large and heterogeneous collection of mobile applications (apps), smartphones provide a plethora of functionalities and services to billions of users. B ...
EPFL2019Presentation attack detection in voice biometrics
Recent years have shown an increase in both the accuracy of biometric systems and their practical use. The application of biometrics is becoming widespread with fingerprint sensors in smartphones, automatic face recognition in social networks and video-bas ...
The Institution of Engineering and Technology2017Context-Dependent Privacy-Aware Photo Sharing based on Machine Learning
Touradj Ebrahimi, Lin Yuan, Joël Régis Theytaz
Photo privacy has raised a growing concern with the advancements of image analytics, face recognition, and deep learning techniques widely applied on social media. If properly deployed, these powerful techniques can in turn assist people in enhancing their ...
2017Using False Colors to Protect Visual Privacy of Sensitive Content
Many privacy protection tools have been proposed for preserving privacy. Tools for protection of visual privacy available today lack either all or some of the important properties that are expected from such tools. Therefore, in this paper, we propose a si ...
Spie-Int Soc Optical Engineering2015Impact of Eye Detection Error on Face Recognition Performance
Sébastien Marcel, Laurent El Shafey, Abhishek Dutta
The location of the eyes is the most commonly used features to perform face normalization (i.e., alignment of facial features), which is an essential preprocessing stage of many face recognition systems. In this paper, we study the sensitivity of open sour ...
2015