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Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition

Terhorst, Philipp and Kolf, Jan Niklas and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan (2020):
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition.
IEEE, 2020 IEEE International Joint Conference on Biometrics (IJCB), virtual Conference, 28.09.-01.10.2020, ISBN 978-1-7281-9186-7,
DOI: 10.1109/IJCB48548.2020.9304865,
[Conference or Workshop Item]

Abstract

Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embed-dings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Terhorst, Philipp and Kolf, Jan Niklas and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan
Title: Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
Language: English
Abstract:

Face quality assessment aims at estimating the utility of a face image for the purpose of recognition. It is a key factor to achieve high face recognition performances. Currently, the high performance of these face recognition systems come with the cost of a strong bias against demographic and non-demographic sub-groups. Recent work has shown that face quality assessment algorithms should adapt to the deployed face recognition system, in order to achieve highly accurate and robust quality estimations. However, this could lead to a bias transfer towards the face quality assessment leading to discriminatory effects e.g. during enrolment. In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment. Experiments were conducted on two publicly available datasets captured under controlled and uncontrolled circumstances with two popular face embed-dings. We evaluated four state-of-the-art solutions for face quality assessment towards biases to pose, ethnicity, and age. The experiments showed that the face quality assessment solutions assign significantly lower quality values towards subgroups affected by the recognition bias demonstrating that these approaches are biased as well. This raises ethical questions towards fairness and discrimination which future works have to address.

Publisher: IEEE
ISBN: 978-1-7281-9186-7
Uncontrolled Keywords: Biometrics, Machine learning, Artificial intelligence (AI), Face recognition, Image quality, Quality, Bias
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 2020 IEEE International Joint Conference on Biometrics (IJCB)
Event Location: virtual Conference
Event Dates: 28.09.-01.10.2020
Date Deposited: 01 Feb 2021 08:10
DOI: 10.1109/IJCB48548.2020.9304865
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