A Tree System Approach for Fingerprint Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special memorial issue for Professor King-Sun Fu
An approach to fingerprint filter design
Pattern Recognition
Segmentation of fingerprint images—a composite method
Pattern Recognition
Fingerprint image postprocessing: a combined statistical and structural approach
Pattern Recognition
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
A Fingerprint Verification System Based on Triangular Matching and Dynamic Time Warping
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Combination Fingerprint Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Gray-Scale Minutiae Detection In Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Indexing Based on Novel Features of Minutiae Triplets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
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Automatic Fingerprint Identification Systems (AFIS) have various applications to biometric authentication, forensic decision, and many other areas. Fingerprints are useful for biometric purposes because of their well known properties of distinctiveness and persistence over time. Fingerprint images are characterized by alternating spatial distribution of gray-level intensity values of ridges and ravines/valleys of almost equal width. Most of the fingerprint matching techniques require extraction of minutiae that are the terminations and bifurcations of the ridge lines in a fingerprint image. Crucial to this step, is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we focus on binarization of fingerprint images using linear time euclidean distance transform algorithms. We exploit the property of almost equal widths of ridges and valleys for binarization. Computing the width of arbitrary shapes is a non-trivial task. So, we estimate width using distance transform and provide an O(N2 log M) time algorithm for binarization where M is the number of gray-level intensity values in the image and the image dimension is N × N. With M for all purposes being a constant, the algorithm runs in near-linear time in the number of pixels in the image.