On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generative Models for Fingerprint Individuality using Ridge Types
IAS '07 Proceedings of the Third International Symposium on Information Assurance and Security
Statistical Models for Assessing the Individuality of Fingerprints
IEEE Transactions on Information Forensics and Security - Part 1
Latent fingerprint rarity analysis in Madrid bombing case
IWCF'10 Proceedings of the 4th international conference on Computational forensics
A heuristic technique for performance improvement of fingerprint based integrated biometric system
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
| Hi-index | 0.00 |
Fingerprints are considered to be unique because they contain various distinctive features, including minutiae, ridges, pores, etc. Some attempts have been made to model the minutiae in order to get a quantitative measure for uniqueness or individuality of fingerprints. However, these models do not fully exploit information contained in non-minutiae features that is utilized for matching fingerprints in practice. We propose an individuality model that incorporates all three levels of fingerprint features: pattern or class type (Level 1), minutiae and ridges (Level 2), and pores (Level 3). Correlations among these features and their distributions are also taken into account in our model. Experimental results show that the theoretical estimates of fingerprint individuality using our model consistently follow the empirical values based on the public domain NIST-4 database.