This paper introduces a fingerprint identification algorithm by clustering similarity with the view to overcome the dilemmas encountered in fingerprint identification. To decrease multi-spectrum noises in a fingerprin...This paper introduces a fingerprint identification algorithm by clustering similarity with the view to overcome the dilemmas encountered in fingerprint identification. To decrease multi-spectrum noises in a fingerprint, we first use a dyadic scale space (DSS) method for image enhancement. The second step describes the relative features among minutiae by building a minutia-simplex which contains a pair of minutiae and their local associated ridge information, with its transformation-variant and invariant relative features applied for comprehensive similarity measurement and for parameter estimation respectively. The clustering method is employed to estimate the transformation space. Finally, multi-resolution technique is used to find an optimal transformation model for getting the maximal mutual information between the input and the template features. The experimental results including the performance evaluation by the 2nd International Verification Competition in 2002 (FVC2002), over the four fingerprint databases of FVC2002 indicate that our method is promising in an automatic fingerprint identification system (AFIS).展开更多
基金the Project of National Science Fund for Distinguished Young Scholars of China(Grant No.60225008)the National Natural Science Foundation of China(Grant No.60332010) the Project for Young Scientists’Fund of National Natural Science Foundation of China(Grant No.60303022).
文摘This paper introduces a fingerprint identification algorithm by clustering similarity with the view to overcome the dilemmas encountered in fingerprint identification. To decrease multi-spectrum noises in a fingerprint, we first use a dyadic scale space (DSS) method for image enhancement. The second step describes the relative features among minutiae by building a minutia-simplex which contains a pair of minutiae and their local associated ridge information, with its transformation-variant and invariant relative features applied for comprehensive similarity measurement and for parameter estimation respectively. The clustering method is employed to estimate the transformation space. Finally, multi-resolution technique is used to find an optimal transformation model for getting the maximal mutual information between the input and the template features. The experimental results including the performance evaluation by the 2nd International Verification Competition in 2002 (FVC2002), over the four fingerprint databases of FVC2002 indicate that our method is promising in an automatic fingerprint identification system (AFIS).
