| FPQuant: A deep learning-based scalable framework for fingerprint phenomics quantification in large-scale biometric population studies |
| 論文作者 |
Han, ZY; Shi, YL; Zhang, Z; Li, M; Zhang, HG; Tan, JZ; Zhen, WT; Liu, TT; Wang, XY; Wang, CY; Wang, JC; Jin, L; Wang, SJ; Liu, MH; Li, JX |
| 期刊/會議名稱 |
PATTERN RECOGNITION |
| 論文年度 |
2026 |
| 論文類別 |
|
| 摘要 |
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. Crossdatabase 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. |
| 卷 |
173 |
| 影響因子 |
7.6 |
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