Let’s talk about digital identity with Mei Ngan, Scientist at the National Institute of Standards and Technology (NIST).
In episode 42, we explore Mei’s work at NIST evaluating face recognition biometrics with the Face Recognition Vendor Test (FRVT), how accurate facial recognition actually is, and the effects of different variables on the FRVT – face masks (motivated by the pandemic), face morphing as a current FR vulnerability for identity credentials, demographic differentials, and twins – “the forgotten demographic”.
“[Face recognition] technology really has come a long way, especially when you only have half the face available to do recognition with. But with that said though, there still remains certain limitations to the technology – such as being able to differentiate between identical twins, demographic differentials and extremely poor-quality photos.”
Mei Ngan is a scientist at the National Institute of Standards and Technology (NIST). Her research focus includes evaluation of face recognition and tattoo recognition technologies. Mei has authored and co-authored a number of technical publications, including the accuracy of face recognition with face masks, evaluation of face morphing detection algorithms, demographic effects in face recognition, performance of facial age and gender estimation algorithms, and publication of a seminal open tattoo database for developing tattoo recognition research, which she received the Special Contribution Award for at the 2015 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA).
Mei was awarded the Department of Commerce Gold Medal Award in 2020 and was a recipient of the 2020 Women in Biometrics Award, a globally recognised award honouring innovative women in the biometrics field.
Find out more about Mei’s work at nist.gov/programs-projects/face-recognition-vendor-test-frvt
View Women in Identity’s webinar with Mei exploring demographic effects in facial recognition here: https://youtu.be/Lni4Pe8dYuk