FPQuant: A deep learning-based scalable framework for fingerprint phenomics quantification in large-scale biometric population studies

• Developed FPQuant, a multi-task deep learning framework for fingerprint phenotyping. • Achieved 97.18% accuracy in six fingerprint pattern classifications. • Demonstrated 98.63% precision in singularity detection with optimized localization. • Identified unreported geographic variations in fingerprint traits. Fingerprint morphology, while evolutionary conserved yet individually distinct, emerges as a pivotal biometric identifier in anthropological research and forensic investigation. Current methodologies for precise identification and quantification of complex morphological features—particularly ridge counting and mean ridge-furrow pairs ridge breadth—remain constrained by labor-intensive and monolithic pattern recognition systems. This study presents FPQuant (Fingerprint Phenomics Quantification), a multi-task deep learning framework integrating the most comprehensive fingerprint pattern classification, singularity detection, and quantification of 12 morphometric phenotypes to date. Leveraging NSPT database of 28,867 expert-curated fingerprints, FPQuant achieved state-of-the-art performance with 97.18% (6-class), 98.62% (5-class), and 98.67% (4-class) pattern classification accuracy; 98.63% precision in topological singularity detection through optimized discrete keypoint localization; and expert-level precision in critical quantitative measurements including ridge counting. Cross-database validation demonstrated extraordinary generalizability with 96.20% of 5-class accuracy on NIST-4 and 97.75% of singularity precision on FVC2002 DB1. Notably, FPQuant’s integrated phenotypic capability revealed uncharacterized geographic variation in six morphometric traits, establishing novel fingerprint morphometric biomarkers for anthropological research. This study creates a scalable technical paradigm that bridging fingerprint phenomics with large-scale population study, while providing potential new research avenues across anthropology, forensics and biometric authentication.