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Singular point is one of the local fingerprint features, and it is used as a landmark due its scale and rotation immutability. Singular point characteristics have been widely used as a feature vector for many fingerprint classification approaches. This paper introduces a new fingerprint classification method which utilizes a singular point as a reference point to part an input image. The key idea of the proposed classification method is dividing fingerprint into small sub-images using singular point location, and then, creating distinguished patterns for each class using frequency domain representation for each sub-image. The performance evaluation has been conducted for the singular point detection method and the proposed classification algorithm with different database.
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