Automated Framework for Extracting and Restoring Minutiae From Low-Quality Fingerprints

Automated Fingerprint Identification Systems (AFIS) identify individuals swiftly and accurately by extracting distinctive features from fingerprint images. Minutiae, unique markers like ridge endings and bifurcations, are crucial for accurate identification. However, low-quality fingerprints often lack enough high-quality minutiae due to information loss. To address this, we propose a multi-stage minutiae extraction framework comprising a minutiae extractor and a minutiae repairer. The extractor adapts the Cross Stage Partial Network (CSPNet) architecture, integrating multi-scale feature modules to capture fingerprint details at different resolutions. The repairer uses a Transformer-based autoencoder with a 75% random masking strategy to restore missing or corrupted regions. We introduce an automated extraction-restoration-re-extraction process, identifying areas for repair based on the extractor's confidence map. Experiments on benchmark datasets demonstrate the superiority of our method in minutiae extraction accuracy, recall, and speed.