Deep neural networks have become prevalent in human analysis, boosting the performance
of applications, such as biometric recognition, action recognition, as well as person re …

Deepfakes present an emerging threat in cyberspace. Recent developments in machine
learning make deepfakes highly believable, and very difficult to differentiate between what is …

Recent deep face recognition models proposed in the literature utilized large-scale public
datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks …

Face recognition systems have significantly advanced in recent years, driven by the
availability of large-scale datasets. However, several issues have recently came up …

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 …

This study investigates the possibility of mitigating the demographic biases that affect face
recognition technologies through the use of synthetic data. Demographic biases have the …

State-of-the-art face recognition systems require huge amounts of labeled training data.
Given the priority of privacy in face recognition applications, the data is limited to celebrity …

Synthetic biometric samples are created with an ultimate goal of getting around privacy
concerns, mitigating biases in biometric datasets, and reducing the sample acquisition effort …

The advancement of visual intelligence is intrinsically tethered to the availability of data. In
parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic …

Biometric technologies have become an integral component of many personal, commercial,
and governmental identity management systems worldwide. Biometrics rely on highly …