Minutiae Extraction from Fingerprint with Neural Network and Minutiae based Fingerprint Verification

Abstract: Human fingerprints are rich in details called minutiae, which can be used as identification marks for fingerprint verification. The goal of this thesis is to develop a complete system for fingerprint verification through extracting and matching minutiae. A neural network is trained using the back-propagation algorithm and will work as a classifier to locate various minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of binarization and skeletonization is first applied on fingerprints before they are evaluated by the neural network. Extracted minutiae are then considered as a 2D point pattern problem and an algorithm is used to determine the number of matching points between two point patterns. Performance of the developed system is evaluated on a database with fingerprints from different people and experimental results are presented.