Author
Abstract
Manual fingerprint classification algorithms are very time consuming, and usually not accurate. Fast and accurate fingerprint classification is essential to each AFIS (Automatic Fingerprint Identification System). This paper investigates a fingerprint classification algorithm that reduces the complexity and costs associated with the fingerprint identification procedure. A new structural algorithm for classification of fingerprints is described. This algorithm is based on structural features: core and delta, and their orientation. The accuracy and speed of the proposed method is tested for a large number of fingerprint images with different initial qualities. The results are independent of image orientation and, show a significant classification performance.
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