Fingerprint minutiae matching using the adjacent feature vector

Minutiae matching is the most popular approach to fingerprint verification. In this paper, we propose a novel fingerprint feature named the adjacent feature vector (AFV) for fingerprint matching. An AFV consists of four adjacent relative orientations and six ridge counts of a minutia. Given a fingerprint image, the optimal matching score is computed in three stages: (1) minutiae candidate pairs searching based on AFVs; (2) coordinate transform for image rotation and translation; and (3) transformed minutiae matching to get matching score. The experimental results show that the proposed method provides a good trade-off between speed and accuracy.