Deep learning (DL) systems have been gaining popularity in critical tasks such as credit
evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL …

Face Recognition is considered one of the most common biometric solutions these days and
is widely used across a range of devices for various security purposes. The performance of …

Face recognition (FR) systems have a growing effect on critical decision-making processes.
Recent works have shown that FR solutions show strong performance differences based on …

Over the past two decades, biometric recognition has exploded into a plethora of different
applications around the globe. This proliferation can be attributed to the high levels of …

Deep learning algorithms provide state-of-the-art results on a multitude of applications.
However, it is also well established that they are highly vulnerable to adversarial …

Despite having completely different configurations deep learning architectures learn a
specific set of features that are common across architectures. For example the initial few …

Driven by the advances in deep learning, highly photo-realistic techniques capable of
switching the identity and expression of faces have emerged. Cheap access to computing …

Adversarial Examples (AEs) can deceive Deep Neural Networks (DNNs) and have received
a lot of attention recently. However, majority of the research on AEs is in the digital domain …

Face recognition (FR) systems have demonstrated reliable verification performance,
suggesting suitability for real-world applications ranging from photo tagging in social media …

This work focuses on facial processing, which refers to artificial intelligence (AI) systems that
take facial images or videos as input data and perform some AI-driven processing to obtain …