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Fingerprints are widely used for unique personal identification based on minutiae matching. Minutiae are the terminations and bifurcations of ridges in a fingerprint image. Generally fingerprint images are of low quality due to the presence of noise and contrast deficiency resulting in discontinuity in ridges producing false minutiae points. It is worth noting that there is a fundamental difference between a neural network (NN) approach for minutiae location and minutiae filtering. In this paper, the spurious minutiae points and the bug pixels introduced during the thinning process are eliminated based on the neighborhood pixel information. A new minutiae filtering algorithm using a NN is introduced to improve the accuracy of the extraction algorithm proposed in the literature. Each minutia, as detected by the algorithm, is classified through ARTMAP NN whose output indicates whether it is a termination, a bifurcation or a false minutia. Experimental results show that the efficiency of minutiae classification has significantly improved using the proposed filtering algorithm. [ABSTRACT FROM AUTHOR]

It is shown that the multi-Veneziano amplitude is the generating function of multiple partitions. Recursion relations for the multi-Veneziano amplitude and the residue functions are explicitly given. [ABSTRACT FROM AUTHOR]