2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
Is Facial Recognition Biased at Near-Infrared Spectrum As Well?
Anoop Krishnan, Brian Neas, Ajita Rattani
Published academic research and media articles suggest face recognition is biased across demographics. Specifically, unequal performance is obtained for women, dark-skinned people,…
Investigating Fairness of Ocular Biometrics Among Young, Middle-Aged, and Older Adults
Anoop Krishnan, Ali Almadan, Ajita Rattani
A number of studies suggest bias of the face biometrics, i.e., face recognition and soft-biometric estimation methods, across gender, race, and age groups. There is a recent urge t…
Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset
Anoop Krishnan, Ali Almadan, Ajita Rattani
Recent research has questioned the fairness of face-based recognition and attribute classification methods (such as gender and race) for dark-skinned people and women. Ocular biome…
Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups
Anoop Krishnan, Ali Almadan, Ajita Rattani
Automated gender classification has important applications in many domains, such as demographic research, law enforcement, online advertising, as well as human-computer interaction…
BWCFace: Open-set Face Recognition using Body-worn Camera
Ali Almadan, Anoop Krishnan, Ajita Rattani
With computer vision reaching an inflection point in the past decade, face recognition technology has become pervasive in policing, intelligence gathering, and consumer application…