Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI)
algorithms present promising solutions to a variety of technological and non-technological …

Recent studies have demonstrated that most commercial facial analysis systems are biased
against certain categories of race, ethnicity, culture, age and gender. The bias can be traced …

Existing public face image datasets are strongly biased toward Caucasian faces, and other
races (eg, Latino) are significantly underrepresented. The models trained from such datasets …

Fairness in deep learning has attracted tremendous attention recently, as deep learning is
increasingly being used in high-stake decision making applications that affect individual …

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

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 …

Race and gender have long sociopolitical histories of classification in technical
infrastructures-from the passport to social media. Facial analysis technologies are …

In this paper, we examine computational approaches for measuring the" fairness" of image
tagging systems, finding that they cluster into five distinct categories, each with its own …

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 …

Emotion recognition has gained prominence in diverse applications ranging from safe
driving and e-commerce to healthcare. Traditional approaches have often relied on single …