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Title: Evaluation of Gender Bias in Facial Recognition with Traditional Machine Learning Algorithms
The prevalent commercial deployment of automated facial analysis systems such as face recognition as a robust authentication method has increasingly fueled scientific attention. Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of age, race, and gender. Algorithms with such biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this paper, we study the gender bias in facial recognition with gender balanced and imbalanced training sets using five traditional machine learning algorithms. We aim to report the machine learning classifiers which are inclined towards gender bias and the ones which mitigate it. Miss rates metric is effective in finding out potential bias in predictions. Our study utilizes miss rates metric along with a standard metric such as accuracy, precision or recall to evaluate possible gender bias effectively.  more » « less
Award ID(s):
1900087
NSF-PAR ID:
10358566
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IEEE Symposium Series on Computational Intelligence (SSCI)
Page Range / eLocation ID:
1 to 7
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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