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
Fingerprint Image Postprocessing Using Windowing Technique
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Handbook of Fingerprint Recognition
Handbook of Fingerprint Recognition
Handbook of Multibiometrics
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
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Accuracy and reliability are two terms that are vital in a biometric system, which must also tolerate the fuzziness of the biometric 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 for fuzzy vault implementation. In this pre-processing stage, we attempt to recover and enhance the corrupted and noisy region by employing filtering technique. The enhanced image is finally transformed to its skeleton equivalent, preserving the ridges and valleys connectivity for minutiae extraction process. Rutovitz Crossing Number (CN) algorithm 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 procedures. We then perform the fingerprint fuzzy vault cryptography processes based on the extracted minutiae, where a secret key is generated, encoded and then decoded. Experiments have been conducted for the fingerprint image processing stage and fuzzy vault implementation stage. We obtained a Goodness Index (GI) of 0.55 for the image processing stage, which indicates that our implementation is performing well comparing to other methods. As for the fuzzy vault implementation, we managed to achieve promising False Acceptance Rate (FAR) and False Rejection Rate (FRR) for polynomial degrees ranging from 8 to