A self-supervised segmentation model detects synthetic fingerprint mosaicking artifacts and a new score quantifies their severity, but real-artifact validation is missing.
More than Zero: Accounting for Error in Latent Fingerprint Identification
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Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach
A self-supervised segmentation model detects synthetic fingerprint mosaicking artifacts and a new score quantifies their severity, but real-artifact validation is missing.