Can I read Mask-up: Investigating Biases in Face Re-identification for Masked Faces on EtoBox?

Mask-up: Investigating Biases in Face Re-identification for Masked Faces by Jaiswal, Siddharth D; Verma, Ankit Kr.; Mukherjee, Animesh is a scholarly article available to read on EtoBox.

What is Mask-up: Investigating Biases in Face Re-identification for Masked Faces about?

AI based Face Recognition Systems (FRSs) are now widely distributed and deployed as MLaaS solutions all over the world, moreso since the COVID-19 pandemic for tasks ranging from validating individuals' faces while buying SIM cards to surveillance of citizens. Extensive biases have been reported against marginalized groups in these systems and have led to highly discriminatory outcomes. The post-pandemic world has normalized wearing face masks but FRSs have not kept up with the changing times. As a result, these systems are susceptible to mask based face occlusion. In this study, we audit four commercial and nine open-source FRSs for the task of face re-identification between different varieties of masked and unmasked images across five benchmark datasets (total 14,722 images). These simulate a realistic validation/surveillance task as deployed in all major countries around the world. Three of the commercial and five of the open-source FRSs are highly inaccurate; they further perpetuate biases against non-White individuals, with the lowest accuracy being 0%. A survey for the same task with 85 human participants also results in a low accuracy of 40%. Thus a human-in-the-loop moderati

Author

Jaiswal, Siddharth D; Verma, Ankit Kr.; Mukherjee, Animesh

Published

2024

Language

EN