26.2 Computer Vision Privacy

Face recognition is becoming widely used in government and business for a variety of tasks including border control, crowd control, shopper identification, and so on. A significant issue for such models is to deal with the bias of the models often resulting from the bias inherent in the dataset of images provided for the training of the model.

The US National Institute of Standards and Techology (NIST) has considered issues of bias and effectiveness of face recognition models. For example, during the COVID-19 pandemic NIST investigated the impact of masks on identification but automatically masking images of millions of faces and testing the effectiveness.

See https://www.nist.gov/programs-projects/face-recognition-vendor-test-frvt

https://www.wired.com/story/schools-adopt-face-recognition-name-fighting-covid/

---

If you find this curated material useful then you can consider a donation to support it's ongoing availability and give you access to the PDF version of this book. The material has been scoped up by Generative AI without permission or any kind of recompense so do consider a donation if you can afford it. Unlike Generative AI your access to this materials is freely given. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Togaware has a 30 year tradition of making popular open source software which includes sold privacy preserving productivity apps, rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0