Assessing Bias in Face Image Quality Assessment
\v{Z}iga Babnik, Vitomir \v{S}truc

TL;DR
This study evaluates demographic biases in face image quality assessment methods across different face recognition systems, revealing biases favoring white individuals and a trade-off between bias reduction and overall performance.
Contribution
It provides a comprehensive analysis of demographic biases in various FIQA techniques and their dependence on underlying face recognition models.
Findings
All FIQA methods are more affected by race than sex.
General-purpose IQA methods are less biased than face-specific methods.
Less biased methods tend to have lower overall face recognition performance.
Abstract
Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias. It is therefore likely that such problems are also present with FIQA techniques. To investigate the demographic biases associated with FIQA approaches, this paper presents a comprehensive study involving a variety of quality assessment methods (general-purpose image quality assessment, supervised face quality assessment, and unsupervised face quality assessment methods) and three diverse…
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Taxonomy
TopicsFace recognition and analysis · Retinal and Optic Conditions · Facial Nerve Paralysis Treatment and Research
