The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …

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 performance of a computer vision model depends on the size and quality of its training
data. Recent studies have unveiled previously-unknown composition biases in common …

Recognition of expressions of emotions and affect from facial images is a well-studied
research problem in the fields of affective computing and computer vision with a large …

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 …

Computer Vision (CV) has achieved remarkable results, outperforming humans in several
tasks. Nonetheless, it may result in significant discrimination if not handled properly. Indeed …

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 paper introduces a novel dataset to help researchers evaluate their computer vision
and audio models for accuracy across a diverse set of age, genders, apparent skin tones …

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 …