Abstract
This thesis combines different fingerprint matching methods which are based on different features of the fingerprint image. The features on the fingerprint image that we used to matching are minutiae, ridge features and block skeleton image. The minutiae are defined as the ending points or bifurcation of the ridges, and the information used to match is their coordinates and orientation. The ridge features contain ridge count, ridge length, ridge curvature direction and ridge frequency. The ridge features are extracted in block around each minutia. In block skeleton image, we cut the skeleton image into a 130x130 block which takes a minutia as a center. The image registration finds the rotation angle of the cut blocks around the minutiae pair which is from the input fingerprint and query fingerprint. After rotation, we calculate the similarity of the two block skeleton image. The similarity scores of the three features summed with different weighting value as the final score of two fingerprints. Experiments are conducted for the FVC2002 database to compare the proposed method with other fingerprint methods on equal error rate (EER). The proposed method achieves better performance. The average EER value in the database of proposed method is 0.82 only and the average EER value of the conventional matching method using minutiae is 8.12.