The performance of face analysis and recognition systems depends on the quality of the
acquired face data, which is influenced by numerous factors. Automatically assessing the …

The presence of decision-making algorithms in society is rapidly increasing nowadays,
while concerns about their transparency and the possibility of these algorithms becoming …

Systems incorporating biometric technologies have become ubiquitous in personal,
commercial, and governmental identity management applications. Both cooperative (eg …

Previous generations of face recognition algorithms differ in accuracy for images of different
races (race bias). Here, we present the possible underlying factors (data-driven and …

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 …

This work proposes a novel privacy-preserving neural network feature representation to
suppress the sensitive information of a learned space while maintaining the utility of the …

Deep learning has catalysed progress in tasks such as face recognition and analysis,
leading to a quick integration of technological solutions in multiple layers of our society …

This work explores the biases in learning processes based on deep neural network
architectures. We analyze how bias affects deep learning processes through a toy example …

We propose a discrimination-aware learning method to improve both the accuracy and
fairness of biased face recognition algorithms. The most popular face recognition …