  Image Preprocessing is a vital step in the field of image processing for
biometric pattern recognition. This paper studies and reviews various classical
and modern fingerprint image de-noising models. The various model used for
de-noising ranges widely from transform matrix using frequency, histogram model
de-noising, de-noising by introducing Gabor filter and its types to enhance
fingerprint images.
  The output efficiency of various de-noising model proposed earlier is
calculated on the basis of SNR (signal to noise ratio) and MSE (mean square
error rate). Our simulated experimental results indicates that incorporating
the de-noising model based on Gabor filter inside domain of wavelet ranges with
composite method only betters MSE (Mean Square Error). Improved MSE without
significant improvement in SNR improves the fingerprint images only by a little
margin which is non-optimal in nature. Thus the objective of this research
paper is to build an optimal de-noising model for fingerprint images so that
its usage in biometric authentication can be more robust in nature.
