Fairness Matters – A Data-Driven Framework Towards Fair and High Performing Facial Recognition Systems
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by
Yushi Cao, David Berend, Palina Tolmach, Moshe Levy, Guy Amit, Asaf Shabtai, Yuval Elovici, Yang Liu
2020
Abstract
Facial recognition technologies are widely used in governmental and
industrial applications. Together with the advancements in deep learning (DL),
human-centric tasks such as accurate age prediction based on face images become
feasible. However, the issue of fairness when predicting the age for different
ethnicity and gender remains an open problem. Policing systems use age to
estimate the likelihood of someone to commit a crime, where younger suspects
tend to be more likely involved. Unfair age prediction may lead to unfair
treatment of humans not only in crime prevention but also in marketing,
identity acquisition and authentication. Therefore, this work follows two
parts. First, an empirical study is conducted evaluating performance and
fairness of state-of-the-art systems for age prediction including baseline and
most recent works of academia and the main industrial service providers (Amazon
AWS and Microsoft Azure). Building on the findings we present a novel approach
to mitigate unfairness and enhance performance, using distribution-aware
dataset curation and augmentation. Distribution-awareness is based on
out-of-distribution detection which is utilized to validate equal and diverse
DL system behavior towards e.g. ethnicity and gender. In total we train 24 DNN
models and utilize one million data points to assess performance and fairness
of the state-of-the-art for face recognition algorithms. We demonstrate an
improvement in mean absolute age prediction error from 7.70 to 3.39 years and a
4-fold increase in fairness towards ethnicity when compared to related work.
Utilizing the presented methodology we are able to outperform leading industry
players such as Amazon AWS or Microsoft Azure in both fairness and age
prediction accuracy and provide the necessary guidelines to assess quality and
enhance face recognition systems based on DL techniques.
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