Face ID | EPFL Graph Search

Face ID is a facial recognition system designed and developed by Apple Inc. for the iPhone and iPad Pro. The system allows biometric authentication for unlocking a device, making payments, accessing sensitive data, providing detailed facial expression tracking for Animoji, as well as six degrees of freedom (6DOF) head-tracking, eye-tracking, and other features. Initially released in November 2017 with the iPhone X, it has since been updated and introduced to several new iPhone models, and all iPad Pro models. The Face ID hardware consists of a sensor with three modules; a laser dot projector that projects a grid of small infrared dots onto a user's face, a module called the flood illuminator that shines infrared light at the face, and an infrared camera which takes an infrared picture of the user, reads the resulting pattern and generates a 3D facial map. This map is compared with the registered face using a secure subsystem, and the user is authenticated if the two faces match sufficiently. The system can recognize faces with glasses, clothing, makeup, and facial hair, and adapts to changes in appearance over time. Face ID has sparked a number of debates about security and privacy. Apple claims that Face ID is statistically more advanced than Touch ID fingerprint scanning. It exhibits significantly fewer false positives. Still, Face ID has shown issues at separating identical twins. Multiple security features largely limit the risk of the system being bypassed using photos or masks, and only one proof-of-concept attempt using detailed scans has succeeded. Debate continues over the lack of legal protections offered by biometric systems as compared to passcode authentication in the United States. Privacy advocates have also expressed concern about third-party app developers' access to "rough maps" of user facial data, despite rigid requirements by Apple of how developers handle facial data. With the onset of the COVID-19 pandemic, it was noted that Face ID was unable to recognize users wearing face coverings on some devices.

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

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

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

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Touradj Ebrahimi, Yuhang Lu, Zewei Xu

Recent years have witnessed significant advance- ment in face recognition (FR) techniques, with their applications widely spread in people’s lives and security-sensitive areas. There is a growing need for reliable interpretations of decisions of such syste ...

2024

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 ...

Wiley2024