The first step is normalization, which results in a better contrast of the fingerprint image. After that, the fingerprint is segmented, which crops areas of the recorded image, which do not contain any relevant information. The last pre-processing step usually consists of fingerprint enhancements. 
Once the classifier filters out the bad inputs, we will be using the classified inputs as an input parameter to our minutiae verification algorithm which takes the input finds the ridges/points of the minutiae of the fingerprint provided. Then these points are then used and compared to points already available in the database by matching the similar based on their minutiae.

Frequency & Setting