Fingerprint recognition is one of the biometric techniques that are used for identification purpose. Human fingerprints are rich in details called minutiae, whic can be used as identification marks for fingerprint identification. The goal of this project is to develop a complete system for fingerprint recognition througn extracting and matching minutiae. To achieve good minutiae extraction in fingerprints with varying quality, preprocessing in form of image enhancement and binarization is first applied on fingerprints before they are evaluated. This research presents a combination of global( core point detection) and local minutiae ( termination and bifurcation) method. The algorithm uses the distance between the minutiae and core points to determine the pattern matching scores for fingerprint images. Experiments were conducted using fingerprint database comprising seven database with different sources and qualities. False Acceptance Rate (FAR) False Rejected Rate (FRR), Genuine Acceptance Rate (GAR) and Genuine Rejected Rate (GRR) were the statistics generated for testing and measuring the performance of the proposed algorithm. The experiment result from DB1_B FVC2002 perform better results than other database.The accuracy of DB1_B is 99,6%.

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