Fingerprint Recognition

The use of biometrics is an evolving component in today’s society. Fingerprint recognition continues to be one of the most widely used biometric systems. This thesis explores the various steps present in a fingerprint recognition system. The study develops a working algorithm to extract fingerprint minutiae from an input fingerprint image. This stage incorporates a variety of image pre-processing steps necessary for accurate minutiae extraction and includes two different methods of ridge thinning. Next, it implements a procedure for matching sets of minutiae data. This process goes through all possible alignments of the datasets and returns the matching score for the best possible alignment. Finally, it conducts a series of matching experiments to compare the performance of the two different thinning methods considered. Results show that thinning by the central line method produces better False Non-match Rates and False Match Rates than those obtained through thinning by the block filter method.