It is shown, for a number of relevant databases, that the Cellular Frequency Amplification (CFA) preprocessing method, introduced by Willis and Myers (2001), improves the FAR/FRR performance for a Precise Biometrics authentication algorithm. This not only corroborates the findings by Willis and Myers, but strengthens them from an authentication perspective: The improved FAR/FRR performance shows that CFA manages to extract the underlying pattern in the noisy fingerprint databases to a larger extent than the four alternative preprocessing methods that were evaluated. These methods were binarization by thresholding, Stock-Swonger binarization, and two proprietary algorithms. CFA performs at least as well as the two selected proprietary preprocessing algorithms, outperforms the public-domain Stock-Swonger algorithm, and far outperforms binarization by thresholding. In the FAR regime 1/1000 to 1/10000 CFA yields FRR values up to one-fifth lower than for the next-best preprocessing method. We find a characteristic scale of 2 mm in the fingerprint, over which no large changes in spatial frequency or direction occur. An image enhancement exponent of k=0.8 is found to be optimal. Larger exponents perform less well due to the fact that higher-order characteristics in the fingerprint pattern are increasingly suppressed together with the structural noise.