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ABSTRACT
<p><b>Abstract</b>—Fingerprint classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve an accuracy better than previously reported in the literature. We classify fingerprints into five categories: whorl, right loop, left loop, arch, and tented arch. The algorithm uses a novel representation (FingerCode) and is based on a two-stage classifier to make a classification. It has been tested on 4,000 images in the NIST-4 database. For the five-class problem, a classification accuracy of 90 percent is achieved (with a 1.8 percent rejection during the feature extraction phase). For the four-class problem (arch and tented arch combined into one class), we are able to achieve a classification accuracy of 94.8 percent (with 1.8 percent rejection). By incorporating a reject option at the classifier, the classification accuracy can be increased to 96 percent for the five-class classification task, and to 97.8 percent for the four-class classification task after a total of 32.5 percent of the images are rejected.</p>
INDEX TERMS
Biometrics, fingerprint classification, Gabor filters, neural networks, FingerCode.
CITATION

L. Hong, S. Prabhakar and A. K. Jain, "A Multichannel Approach to Fingerprint Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 21, no. , pp. 348-359, 1999.
doi:10.1109/34.761265
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