Menu Close

American researchers probe where biometric bias comes in and how to measure it

A pair of papers on why biometric systems are so often found to be less effective with some demographic groups and how to measure those disparities have been published by researchers from the Identity and Data Sciences Lab at the Maryland Test Facility.

John Howard of MdTF, which is used for DHS’ biometrics tests, pointed out the papers in a LinkedIn post.

‘Disparate impact in facial recognition stems from the broad homogeneity effect: A case study and method to resolve’ attributes the problem of biometric bias to “demographic clustering.” This is the phenomenon where the use of features determined (at least in part) by the gender or ethnicity of people increases similarity scores between individuals.

The paper shows that it is possible to remove feature patterns shared within demographic groups while keeping distinct features that can be used for facial recognition. The team used linear dimensionality techniques to increase the “fairness” of two ArcFace algorithms, as measured in four different ways, without lowering true match rates.

‘Evaluating proposed fairness models for face recognition algorithms’ considers the Fairness Discrepancy Rate (FDR) proposed by Idiap researchers and the Inequity Rate (IR) proposed by NIST researchers. Both metrics are found to be difficult to interpret due to inherent mathematical characteristics. The study authors therefore propose the Functional Fairness Measure Criteria (FFMC) to help with interpretations of the above metrics.

They also develop a new measure, the Gini Aggregation Rate for Biometric Equitability (GARBE). This measurement technique is based on the Gini coefficient, which is a statistical measure of dispersion typically used in measuring income inequality.

The work on an evaluation method is intended to directly support ISO 19795-10, which sets an international standard for bias in facial recognition.

Both papers appeared in the publication of the 26th International Conference on Pattern Recognition (ICPR 2022) Fairness in Biometrics Workshop. Read More

Generated by Feedzy


Innov8 is owned and operated by Rolling Rock Ventures. The information on this website is for general information purposes only. Any information obtained from this website should be reviewed with appropriate parties if there is any concern about the details reported herein. Innov8 is not responsible for its contents, accuracies, and any inaccuracies. Nothing on this site should be construed as professional advice for any individual or situation. This website includes information and content from external sites that is attributed accordingly and is not the intellectual property of Innov8. All feeds ("RSS Feed") and/or their contents contain material which is derived in whole or in part from material supplied by third parties and is protected by national and international copyright and trademark laws. The Site processes all information automatically using automated software without any human intervention or screening. Therefore, the Site is not responsible for any (part) of this content. The copyright of the feeds', including pictures and graphics, and its content belongs to its author or publisher.  Views and statements expressed in the content do not necessarily reflect those of Innov8 or its staff. Care and due diligence has been taken to maintain the accuracy of the information provided on this website. However, neither Innov8 nor the owners, attorneys, management, editorial team or any writers or employees are responsible for its content, errors or any consequences arising from use of the information provided on this website. The Site may modify, suspend, or discontinue any aspect of the RSS Feed at any time, including, without limitation, the availability of any Site content.  The User agrees that all RSS Feeds and news articles are for personal use only and that the User may not resell, lease, license, assign, redistribute or otherwise transfer any portion of the RSS Feed without attribution to the Site and to its originating author. The Site does not represent or warrant that every action taken with regard to your account and related activities in connection with the RSS Feed, including, without limitation, the Site Content, will be lawful in any particular jurisdiction. It is incumbent upon the user to know the laws that pertain to you in your jurisdiction and act lawfully at all times when using the RSS Feed, including, without limitation, the Site Content.  

Close Bitnami banner