Fingerprint Identification using Filterbank
An Advance Technique for Fingerprint Identification
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Individual, be it a claimant or an unauthorized person, identification is one of the top challenges of the world. We know that no two individuals have similar fingerprint configuration and this configuration remains constant over the life of a human being. Fingerprint is characterized by a set of ridge lines which often flow parallel, sometimes intersect and sometimes terminate. The points where ridge lines terminate or fork are called minutiae. They can be represented by a vector whose direction is tangent to the ridge line. In this project, we study fingerprint analysis. We present a fingerp...
Individual, be it a claimant or an unauthorized person, identification is one of the top challenges of the world. We know that no two individuals have similar fingerprint configuration and this configuration remains constant over the life of a human being. Fingerprint is characterized by a set of ridge lines which often flow parallel, sometimes intersect and sometimes terminate. The points where ridge lines terminate or fork are called minutiae. They can be represented by a vector whose direction is tangent to the ridge line. In this project, we study fingerprint analysis. We present a fingerprint matching scheme that utilizes a ridge feature map to match fingerprint images. The proposed scheme uses a set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A circular tessellation of filtered image is then used to construct the ridge feature map. The genuine accept rate of the Gabor filter based matcher is observed to be ~ 10% to 15% higher than that of minutiae-based matcher at low false accept rates.
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