Segmentation of fingerprint images—a composite method
Pattern Recognition
Fingerprint image postprocessing: a combined statistical and structural approach
Pattern Recognition
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Direct Gray-Scale Minutiae Detection In Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm for Detection and Elimination of False Minutiae in Fingerprint Images
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
FVC2002: Second Fingerprint Verification Competition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Fingerprint enhancement with dyadic scale-space
Pattern Recognition Letters
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Fingerprint quality indices for predicting authentication performance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Adaptive approach to fingerprint image enhancement
Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia
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Accuracy and reliability are two terms that are vital in a biometrics system, which must also tolerate the fuzziness of the biometrics characteristics to a certain degree. In this paper, we propose and implement fingerprint image enhancement as a preliminary stage to increase the accuracy and reliability of minutiae extraction process. In this pre-processing stage, we attempt to recover and enhance the corrupted and noisy region by employing filtering technique. The enhance image is finally transformed to its skeleton equivalent, preserving the ridges and valleys connectivity for minutiae extraction process. Rutovitz Crossing Number (CN) algorithms is then applied to extract the candidate minutiae which will then undergo a series of minutiae filtering processes to determine the validity of the extracted raw minutiae as true minutia. The implementations of the minutiae filtering processes are able to identify and eliminate the predefined spurious minutiae. As we are focusing on extracting accurate minutiae for the purpose of fuzzy vault implementation, we also take into consideration the quantization of the minutiae, which is an important factor in fuzzy vault locking and unlocking procedure. Experiment results indicate that our implementation methods are able to successfully achieve promising outcomes in terms of accuracy and reliability in minutiae extraction.