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
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
Automated location matching in movies
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Fingerprint Verification Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Warping Using Ridge Curve Correspondences
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Offline Signature Verification Using Local Interest Points and Descriptors
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
A fast probabilistic model for hypothesis rejection in SIFT-Based object recognition
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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A new approach to automatic fingerprint verification based on a general-purpose wide baseline matching methodology is here proposed. The approach is not based on the standard ridge-minutiae-based framework. Instead of detecting and matching the standard structural features, local interest points are detected in the fingerprints, then local descriptors are computed in the neighborhood of these points, and afterwards these descriptors are compared using local and global matching procedures. Then, a final verification is carried out by a Bayes classifier. The methodology is validated using the FVC2004 dataset, where competitive results are obtained.