Fingerprint Recognition Based on Combined Features
In this paper, we represent the fingerprint with a novel local feature descriptor, which is composed of minutia, the sample points on associated ridge and the adjacent orientation distribution. Then a novel fingerprint recognition method is proposed combining the orientation field and the local feature descriptor. We compare two descriptor lists from the input and template fingerprints to calculate a set of transformation vectors for fingerprint alignment. The similarity score is evaluated by fusing the orientation field and the local feature descriptor. The experiments have been conducted on three large-scale databases. The comparison results approve that our algorithm is more accurate and robust than previous methods based on the minutiae or ridge features, especially for those poor-quality and partial fingerprints.
Keywordsorientation field local feature descriptor fingerprint alignment fusing similarity score
- 7.Chen, Y., Dass, S.C., Jain, A.K.: Fingerprint Quality Indices for Predicting Authentication Performance. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 160–170. Springer, Heidelberg (2005)Google Scholar
- 8.Luo, X.P., Tian, J.: Image Enhancement and Minutia Matching Algorithm in Automated Fingerprint Identification System. Journal of software 13 (2002)Google Scholar
- 12.Maio, D., Maltoni, D., Cappelli, R., Wayman, J.L., Jain, A.K.: FVC2004: Third Fingerprint Verification Competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 1–7. Springer, Heidelberg (2004)Google Scholar