摘要
Singular point(SP)extraction is a key component in automatic fingerprint identification system(AFIS).A new method was proposed for fingerprint singular points extraction,based on orientation tensor field and Laurent series.First,fingerprint orientation flow field was obtained,using the gradient of fingerprint image.With these gradients,fingerprint orientation tensor field was calculated.Then,candidate SPs were detected by the cross-correlation energy in multi-scale Gaussian space.The energy was calculated between fingerprint orientation tensor field and Laurent polynomial model.As a global descriptor,the Laurent polynomial coefficients were allowed for rotational invariance.Furthermore,a support vector machine(SVM)classifier was trained to remove spurious SPs,using cross-correlation coefficient as a feature vector.Finally,experiments were performed on Singular Point Detection Competition 2010(SPD2010)database.Compared to the winner algorithm of SPD2010 which has best accuracy of 31.90%,the accuracy of proposed algorithm is 45.34%.The results show that the proposed method outperforms the state-of-the-art detection algorithms by large margin,and the detection is invariant to rotational transformations.
Singular point （SP） extraction is a key component in automatic fingerprint identification system （AFIS）. A new method was proposed for fingerprint singular points extraction, based on orientation tensor field and Laurent series. First, fingerprint orientation flow field was obtained, using the gradient of fingerprint image. With these gradients, fingerprint orientation tensor field was calculated. Then, candidate SPs were detected by the cross-correlation energy in multi-scale Gaussian space. The energy was calculated between fingerprint orientation tensor field and Laurent polynomial model. As a global descriptor, the Laurent polynomial coefficients were allowed for rotational invariance. Furthermore, a support vector machine （SVM） classifier was trained to remove spurious SPs, using cross-correlation coefficient as a feature vector. Finally, experiments were performed on Singular Point Detection Competition 2010 （SPD2010） database. Compared to the winner algorithm of SPD2010 which has best accuracy of 31.90%, the accuracy of proposed algorithm is 45.34%. The results show that the proposed method outperforms the state-of-the-art detection algorithms by large margin, and the detection is invariant to rotational transformations.
基金
Project(11JJ3080)supported by Natural Science Foundation of Hunan Province,China
Project(11CY012)supported by Cultivation in Hunan Colleges and Universities,China
Project(ET51007)supported by Youth Talent in Hunan University,China
