NDLI: The k-NN classifier and self-adaptive Hotelling data reduction technique in handwritten signatures recognition

Content Provider	Springer Nature Link
Author	Porwik, Piotr Doroz, Rafal Orczyk, Tomasz
Copyright Year	2014
Abstract	The paper proposes a novel signature verification concept. This new approach uses appropriate similarity coefficients to evaluate the associations between the signature features. This association, called the new composed feature, enables the calculation of a new form of similarity between objects. The most important advantage of the proposed solution is case-by-case matching of similarity coefficients to a signature features, which can be utilized to assess whether a given signature is genuine or forged. The procedure, as described, has been repeated for each person presented in a signatures database. In the verification stage, a two-class classifier recognizes genuine and forged signatures. In this paper, a broad range of classifiers are evaluated. These classifiers all operate on features observed and computed during the data preparation stage. The set of signature composed features of a given person can be reduced what decrease verification error. Such a phenomenon does not occur for the raw features. The approach proposed was tested in a practical environment, with handwritten signatures used as the objects to be compared. The high level of signature recognition obtained confirms that the proposed methodology is efficient and that it can be adapted to accommodate as yet unknown features. The approach proposed can be incorporated into biometric systems.
Starting Page	983
Ending Page	1001
Page Count	19
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	18
Issue Number	4
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2014-10-14
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Signature verification Hotelling’s statistic Classification Biometrics Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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