Automated fingerprint recognition using structural matching
Pattern Recognition
A Graduated Assignment Algorithm for Graph Matching
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
FVC2000: Fingerprint Verification Competition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
FVC2002: Second Fingerprint Verification Competition
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
Fingerprint Matching Using an Orientation-Based Minutia Descriptor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum-likelihood deformation analysis of different-sized fingerprints
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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This paper proposes a novel method that estimates parameters of a geometric transformation by means of grouping and statistical inference in relative histogram grouping models borrowed from the law of large number Since an image is divided into many local partial objects, each of which is represented by groups of transformation-invariant and transformation-variant (e.g., translation, rotation, and shearing) and whose deformations are considered to be linear, we construct minutiae-simplexes to represent partial objects in a fingerprint A relative histogram grouping model describes the relationship between transformation-invariant and variant Even if the image is transformed at random, partial objects still maintain their linear representation, from which the relative histogram techniques extract grouping centers that account for transformation-variations in partial objects for geometric alignment Our promising experimental results show that our technique is efficient in alignment and match.