2014 10th International Conference on Natural Computation (ICNC) 2014
DOI: 10.1109/icnc.2014.6975917
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Graph based K-nearest neighbor minutiae clustering for fingerprint recognition

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Cited by 5 publications
(4 citation statements)
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“…Unsupervised classification techniques offer numerous benefits over supervised ones in terms of addressing classification challenges. Numerous clustering techniques, such as K-means, spectral clustering, mean shift [ 38 ], and graph theory [ 39 ], are the most often used algorithms. Unsupervised techniques are highly effective yet have poor accuracy since they do not require any prior training or understanding.…”
Section: Road Generationmentioning
confidence: 99%
“…Unsupervised classification techniques offer numerous benefits over supervised ones in terms of addressing classification challenges. Numerous clustering techniques, such as K-means, spectral clustering, mean shift [ 38 ], and graph theory [ 39 ], are the most often used algorithms. Unsupervised techniques are highly effective yet have poor accuracy since they do not require any prior training or understanding.…”
Section: Road Generationmentioning
confidence: 99%
“…The unsupervised road extraction does not need training samples, but instead usually uses clustering algorithms to extract the road networks. The clustering algorithms used in road extraction include K-means, spectral clustering, mean shift [21,22], and graph theory [23,24]. Miao et al proposed a semi-automatic approach using the mean shift to detect roads [22].…”
Section: Road Extractionmentioning
confidence: 99%
“…Classifier K nearest neighbor was used to classify Curvelet co-efficient matrix for reorganization of the specific fingerprint. Vaishali Pawar and Mukesh Zaveri [23] implemented a graph clustering and matching algorithms based fingerprint recognition system. Structural features of the minutiae from the fingerprint were extracted.…”
Section: Short Review On Fingerprint Recognition Techniquesmentioning
confidence: 99%