Rotation and Size Invariant Fingerprint Recognition Using The Neural Net 


Vol. 1,  No. 2, pp. 215-224, Jul.  1994
10.3745/KIPSTE.1994.1.2.215


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  Abstract

In this paper, the rotation and size invariant fingerprint recognition using the neural network EART(Extended Adaptive Resonance Theory) is studied. 512X512 gray level fingerprint images are converted into the binary thinned images based on the adaptive threshold and a thinning algorithm. From these binary thinned images, we extract the ending points and the bifurcation points, which are the most useful critical feature points in the fingerprint images, using the 3X3 MASK. And we convert the number of 40*10 critical matrix using the weighted code which code which is invariant of rotation and size as the input of EART. This system produces very good and efficient results for the rotation and size variations without the restoration of the binary thinned fingerprints

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[IEEE Style]

L. N. Il, W. Y. Tae, L. J. Hwan, "Rotation and Size Invariant Fingerprint Recognition Using The Neural Net," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 1, no. 2, pp. 215-224, 1994. DOI: 10.3745/KIPSTE.1994.1.2.215.

[ACM Style]

Lee Nam Il, Woo Yong Tae, and Lee Jeong Hwan. 1994. Rotation and Size Invariant Fingerprint Recognition Using The Neural Net. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 1, 2, (1994), 215-224. DOI: 10.3745/KIPSTE.1994.1.2.215.

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