A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Classification by Directional Image Partitioning
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
Fingerprint Matching Using Transformation Parameter Clustering
IEEE Computational Science & Engineering
A Triplet Based Approach for Indexing of Fingerprint Database for Identification
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint Classification with Combinations of Support Vector Machines
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint Classification by Combination of Flat and Structural Approaches
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Similarity Metrics Analysis for Feature Point Based Retinal Authentication
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Personal verification based on extraction and characterisation of retinal feature points
Journal of Visual Languages and Computing
Retinal verification using a feature points-based biometric pattern
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Characterisation of Retinal Feature Points Applied to a Biometric System
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Biometric authentication using digital retinal images
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
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Due to the complex distortions involved in two impressions of the same finger, fingerprint identification is still a challenging problem. In this paper, we propose a two step fingerprint identification approach based on the triplets of minutiae. The features that we use to find the potential corresponding triangles include angles, triangle orientation, triangle direction, maximum side, minutiae density and ridge counts. In the first step, based on the number of corresponding triangles between the query fingerprint and the model database constructed offline, hypotheses are generated. In the second step, called verification, false corresponding triangles are eliminated by applying constraints to the transformation between two potential corresponding triangles. The experimental results on National Institute of Standards and Technology special fingerprint database 4, NIST-4, show that the proposed approach provides a reduction by a factor of 10 for the number of the hypotheses that need to be considered if linear search is used and can achieve a good performance even when a large portion of fingerprints in the database are of poor quality.