10 citations · 51 across the 32 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,…
Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups
Sreeraj Ramachandran, Ajita Rattani
Published studies have suggested the bias of automated face-based gender classification algorithms across gender-race groups. Specifically, unequal accuracy rates were obtained for…
GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
Aakash Varma Nadimpalli, Ajita Rattani
Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to effectively combat the misinformation on the intern…
An Examination of Bias of Facial Analysis based BMI Prediction Models
Hera Siddiqui, Ajita Rattani, Karl Ricanek +1
Obesity is one of the most important public health problems that the world is facing today. A recent trend is in the development of intervention tools that predict BMI using facial…
On Improving Cross-dataset Generalization of Deepfake Detectors
Aakash Varma Nadimpalli, Ajita Rattani
Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been propose…