FINGERPRINT IMAGE PROCESSING AND FUZZY VAULT IMPLEMENTATION

Authors

  • NANDITA BHATTACHARJEE Monash University, Melbourne
  • CHIEN EAO LEE Monash University, Melbourne

Keywords:

Biometrics, fingerprint, image processing, minutiae extraction, minutiae filtering, fuzzy vault

Abstract

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 13.

 

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Published

2010-05-11

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