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

机译：手写签名识别中的k-NN分类器和自适应Hotelling数据约简技术

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摘要

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.
机译：本文提出了一种新颖的签名验证概念。这种新方法使用适当的相似性系数来评估签名特征之间的关联。这种关联称为新的合成特征，可以计算对象之间新的相似度形式。所提出的解决方案的最重要的优点是相似系数与签名特征的逐例匹配，这可以用于评估给定签名是真实的还是伪造的。对于签名数据库中显示的每个人，都重复了上述过程。在验证阶段，两级分类器识别真实的和伪造的签名。在本文中，评估了各种各样的分类器。这些分类器均对在数据准备阶段观察和计算的特征进行操作。可以减少给定人的一组签名组成的特征，从而减少验证错误。对于原始特征不会发生这种现象。建议的方法已在实际环境中进行了测试，并以手写签名作为要比较的对象。获得的高水平的签名识别证明了所提出的方法是有效的，并且可以适应于容纳尚未知的特征。建议的方法可以并入生物识别系统。

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