A genetic algorithm is developed to find the ridges in paper fingerprints. It is based on the fact that the ridges of the fingerprints are parallel. When scanning the fingerprint, line by line, the ideal noise-free gray level distribution should yield lines of black and white. The widths of these lines are not constant. The proposed genetic algorithm generates black and white lines of different widths. The widths change until we get the best match with the original fingerprint.

The objective of this report is to investigate the possibility of using random autoregressive (AR) models in signal processing problems. The advantage of this approach is that they may reflect more accurately the uncertainity in the stochastic process that generates the received signal. The method is applied to adaptive line enhancement where it is shown through Monte Carlo simulations, that it yields better results than the conventional deterministic AR models under the same conditions.