A Real-Time Matching System for Large Fingerprint Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
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
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Automatic Fingerprint Recognition Systems
Automatic Fingerprint Recognition Systems
Fingerprint Matching Based on Global Comprehensive Similarity
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
Handbook of Biometrics
High resolution partial fingerprint alignment using pore-valley descriptors
Pattern Recognition
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
A novel hierarchical fingerprint matching approach
Pattern Recognition
Phase congruency induced local features for finger-knuckle-print recognition
Pattern Recognition
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Sweat pores on fingerprints have proven to be discriminative features and have recently been successfully employed in automatic fingerprint recognition systems (AFRS), where the extraction of fingerprint pores is a critical step. Most of the existing pore extraction methods detect pores by using a static isotropic pore model; however, their detection accuracy is not satisfactory due to the limited approximation capability of static isotropic models to various types of pores. This paper presents a dynamic anisotropic pore model to describe pores more accurately by using orientation and scale parameters. An adaptive pore extraction method is then developed based on the proposed dynamic anisotropic pore model. The fingerprint image is first partitioned into well-defined, ill-posed, and background blocks. According to the dominant ridge orientation and frequency on each foreground block, a local instantiation of appropriate pore model is obtained. Finally, the pores are extracted by filtering the block with the adaptively generated pore model. Extensive experiments are performed on the high resolution fingerprint databases we established. The results demonstrate that the proposed method can detect pores more accurately and robustly, and consequently improve the fingerprint recognition accuracy of pore-based AFRS.