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Evaluating Face Image Quality Score Fusion for Modern Deep Learning Models
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Datum
2022
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Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Face image quality assessment algorithms attempt to estimate the utility of face images for
biometric systems, typically face recognition, since the performance of these systems can be limited
by the image quality. Hand-crafted quality score fusion has previously been examined for a variety
of mostly factor-specific quality assessment algorithms. This paper instead examines score fusion for
various recent “monolithic” quality assessment deep learning models. The evaluation methodology
is based on Error-versus-Reject-Characteristic partial-Area-Under-Curve values, which are used to
quantitatively rank quality assessment configurations in a face recognition context. Mean quality
score fusion configurations were found to slightly improve performance on the TinyFace database,
while the tested fusion types were ineffective on the LFW database.