Fingerprint Matching Incorporating Ridge Features Using Wavelet Transform

Next to DNA, fingerprint is the unique feature which identifies the individual. Due to distortions such as skin elasticity, non uniform pressure applied by the subject, different finger placement with the sensor and deformations in the skin, the fingerprint is difficult to match in the conventional minutiae only approach. But when ridge features are incorporated with minutiae features (minutiae type, orientation and position) more topological information is obtained. And also ridges are invariant to transformations such as rotation and translation. Ridge based coordinate system is used to extract the ridge features such as ridge length, ridge count, ridge type and curvature direction in the skeletonized image. Breadth First Search is used to traverse the graph formed using the minutiae as the node and the ridge vector formed using the ridge features as the edge. The proposed ridge feature gives additional information for fingerprint matching with little increment in template size and can be used along with the existing minutiae features to increase the accuracy and robustness of fingerprint recognition systems.