Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Fingerprint image postprocessing: a combined statistical and structural approach
Pattern Recognition
A Real-Time Matching System for Large Fingerprint Databases
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
Introduction to Algorithms
Fingerprint Matching Using Transformation Parameter Clustering
IEEE Computational Science & Engineering
Image enhancement and minutiae matching in fingerprint verification
Pattern Recognition Letters
A modified Gabor filter design method for fingerprint image enhancement
Pattern Recognition Letters
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Integrating Local and Global Features in Automatic Fingerprint Verification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A Minutia Matching Algorithm in Fingerprint Verification
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Approximate Fingerprint Matching Using Kd-Tree
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Fingerprint Warping Using Ridge Curve Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint enhancement using STFT analysis
Pattern Recognition
On the representation of a digital contour with an unordered point set for visual perception
Journal of Visual Communication and Image Representation
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Most of the contemporary automatic fingerprint identification systems (AFIS) are based on a dual strategy of combining the minutiae information with the ridge topography in order to improve the overall matching performance. To ensure the efficiency and robustness of such an AFIS, it is necessary, therefore, to rectify the abnormalities or aberrations of the underlying ridge topography, in general, and to smoothen the uneven/noisy ridgelines, in particular. The proposed work deals with one such problem besetting fingerprint analysis-the problem of eliminating digitization errors that usually creep in during fingerprint acquisition or during preprocessing. The method mainly involves fitting of B-splines for a set of control points chosen appropriately for each ridgeline in a fingerprint image. These fitted splines, in turn, can be used to reconstruct the concerned fingerprint, which, after the rectification procedure, becomes almost devoid of such digitization error. With a proper ''smoothness parameter'' that determines the extent to which a ridgeline is smoothed, the structural information of the corrected ridgelines produces improved results on fingerprint matching. Experimental results on several databases have been reported, which clearly demonstrate the strength and elegance of the proposed algorithm.