Fingerprint pattern classification
Pattern Recognition
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Definition and extraction of stable points from fingerprint images
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint reference-point detection
EURASIP Journal on Applied Signal Processing
Beyond Minutiae: A Fingerprint Individuality Model with Pattern, Ridge and Pore Features
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Probability of Random Correspondence for Fingerprints
IWCF '09 Proceedings of the 3rd International Workshop on Computational Forensics
Latent Fingerprint Core Point Prediction Based on Gaussian Processes
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Statistical Models for Assessing the Individuality of Fingerprints
IEEE Transactions on Information Forensics and Security - Part 1
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
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Rarity of latent fingerprints is important to law enforcement agencies in forensics analysis. While tremendous efforts have been made in 10-print individuality studies, latent fingerprint rarity continues to be a difficult problem and has never been solved because of the small finger area and poor impression quality. The proposed method is able to predict the core points of latent prints using Gaussian processes and align the latent prints by overlapping the core points. A novel generative model is also proposed to take into account the dependency on nearby minutiae and the confidence of minutiae in the probability of random correspondence calculation. The new methods are illustrated by experiments on the well-known Madrid bombing case. The results show that the probability that at least one fingerprint in the FBI IAFIS databases (over 470 million fingerprints) matches the bomb site latent is 0.93 which is large enough to lead to misidentification.