Face recognition in humans and machines

This repository contains code for analyses to measure human and algorithm performance on face recognition and face matching tasks.

Forensic facial examiners vs. super-recognizers: Evaluating behavior beyond accuracy

• Code: GitHub
• Paper: Hahn, C. A., Tang, L. L., Yates, A. N., & Phillips, P. J. (2022). Forensic facial examiners versus super-recognizers: Evaluating behavior beyond accuracy. Applied Cognitive Psychology, 1– 10. https://doi.org/10.1002/acp.4003

Who Is That? Perceptual Expertise on Other-Race Face Comparisons, Disguised Face Comparisons, and Face Memory

• Code: GitHub
• Paper: Yates, A. N., Cavazos, J. G., Jeckeln, G., Hu, Y., Noyes, E., Hahn, C. A., O'Toole, A. J., & Phillips, P. J. (2023). Who Is That? Perceptual Expertise on Other-Race Face Comparisons, Disguised Face Comparisons, and Face Memory. PsyArXiv Preprint. https://doi.org/10.31234/osf.io/s87na

Other work

For FRTE and FATE (formerly FRVT), see usnistgov/frvt