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The Demographic Effects of Facial Recognition

The Demographic Effects of Facial Recognition

The Demographic Effects of Facial Recognition
Sept 17, 2020, 4-5pm BST

The recent expansion in the availability, capability and use of facial recognition has been accompanied by assertions that demographic dependencies could lead to accuracy variations and potential bias. The National Institute of Standards and Technology (NIST) conducted tests to quantify demographic differences in contemporary facial recognition algorithms. This webinar will present the outcomes of the study published in the report – FRVT Part 3: Demographic Effects, providing details about the recognition process, where demographic effects could occur, specific performance metrics and analyses, empirical results, and recommendations for research into the mitigation of performance deficiencies.

Mei Ngan is a research scientist at NIST.  Her research focus includes evaluation of face recognition and tattoo recognition technologies.  She is currently involved in a number of key face recognition testing activities at NIST, including leading the Face Recognition Vendor Test (FRVT) MORPH project to evaluate face morphing detection algorithms. Mei has authored and co-authored a number of technical publications, including the accuracy of face recognition with face masks, demographic effects in face recognition, performance of facial age and gender estimation algorithms. She also worked on a seminal open tattoo database for developing tattoo recognition research. This won her a Special Contribution Award at the 2015 IEEE International Conference on I Security and Behavior Analysis (ISBA). In 2020 she received a Women in Biometrics award.

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