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A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Ashish Mishra, Preeti Maheshwary
10.5120/ijca2017914806

Ashish Mishra and Preeti Maheshwary. A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine. International Journal of Computer Applications 169(7):58-62, July 2017. BibTeX

@article{10.5120/ijca2017914806,
	author = {Ashish Mishra and Preeti Maheshwary},
	title = {A Novel Technique for Fingerprint Classification based on Naive Bayes Classifier and Support Vector Machine},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {7},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {58-62},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume169/number7/28001-2017914806},
	doi = {10.5120/ijca2017914806},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Fingerprint classification decreases the number of possible matches in automated fingerprint identification systems by categorizing fingerprints into predefined classes. Support vector machines are widely used in pattern classification and have produced high accuracy when performing fingerprint classification. In order to effectively apply Support vector machines to multi-class fingerprint classification systems.It is proposed a novel method in which the fingerprint classification can be done by the classifier used Naïve Bayes and Support vector machines efficiently reduce the search time by restricting the subsequent searching stage to either left hand thumb and right hand thumb databases.

References

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Keywords

Fingerprint classification, Support vector machine; FingerCode; Naïve Bayes classifier; classifier combination, directional image, feature selection, subspace classifiers.