Direct Gray-Scale Minutiae Detection In Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
FVC2002: Second Fingerprint Verification Competition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Automatic Fingerprint Recognition Systems
Automatic Fingerprint Recognition Systems
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pores and Ridges: Fingerprint Matching Using Level 3 Features
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining minutiae descriptors for fingerprint matching
Pattern Recognition
Segmentation of fingerprint images using linear classifier
EURASIP Journal on Applied Signal Processing
Outlier Robust ICP for Minimizing Fractional RMSD
3DIM '07 Proceedings of the Sixth International Conference on 3-D Digital Imaging and Modeling
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
Robust point-based feature fingerprint segmentation algorithm
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
A novel hierarchical fingerprint matching approach
Pattern Recognition
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Sweat pores on fingerprints have proven to be useful features for personal identification. Several methods have been proposed for pore matching. The state-of-the-art method first matches minutiae on the fingerprints and then matches the pores based on the minutia matching results. A problem of such minutia-based pore matching method is that the pore matching is dependent on the minutia matching. Such dependency limits the pore matching performance and impairs the effectiveness of the fusion of minutia and pore match scores. In this paper, we propose a novel direct approach for matching fingerprint pores. It first determines the correspondences between pores based on their local features. It then uses the RANSAC (RANdom SAmple Consensus) algorithm to refine the pore correspondences obtained in the first step. A similarity score is finally calculated based on the pore matching results. The proposed pore matching method successfully avoids the dependency of pore matching on minutia matching results. Experiments have shown that the fingerprint recognition accuracy can be greatly improved by using the method proposed in this paper.