6 papers
Rank-1 Identity Consensus Predicts Gallery Enrollment in 1:N Face Matching More Accurately than Score Thresholding
Gabriella Pangelinan, Aman Bhatta, Michael C. King +1
In operational 1:N face identification, a crucial question arises for each probe: is this person enrolled in the gallery or not? The stakes are high and asymmetric. Rejecting a mat…
Peepers & Pixels: Human Recognition Accuracy on Low Resolution Faces
Xavier Merino, Gabriella Pangelinan, Samuel Langborgh +2
Automated one-to-many (1:N) face recognition is a powerful investigative tool commonly used by law enforcement agencies. In this context, potential matches resulting from automated…
Lights, Camera, Matching: The Role of Image Illumination in Fair Face Recognition
Gabriella Pangelinan, Grace Bezold, Haiyu Wu +2
Facial brightness is a key image quality factor impacting face recognition accuracy differentials across demographic groups. In this work, we aim to decrease the accuracy gap betwe…
Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification
Aman Bhatta, Gabriella Pangelinan, Michael C. King +1
Most studies to date that have examined demographic variations in face recognition accuracy have analyzed 1-to-1 matching accuracy, using images that could be described as "governm…
Analysis of Adversarial Image Manipulations
Ahsi Lo, Gabriella Pangelinan, Michael C. King
As virtual and physical identity grow increasingly intertwined, the importance of privacy and security in the online sphere becomes paramount. In recent years, multiple news storie…
Exploring Causes of Demographic Variations In Face Recognition Accuracy
Gabriella Pangelinan, K. S. Krishnapriya, Vitor Albiero +5
In recent years, media reports have called out bias and racism in face recognition technology. We review experimental results exploring several speculated causes for asymmetric cro…