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EN
Traditional use of dynamic time warping for signature verification consists of forming some dissimilarity measure between the signature in question and a set of "template signatures". In this paper, we propose to replace this set with the hidden signature and use it to calculate the normalized errors of signature under verification. The approach was tested on the MCYT database, using both genuine signatures and skilled forgeries. Moreover, we present the real-world application of the proposed algorithm, namely the complete biometric system for authorizing payment transactions. The authorization is performed directly at a point of sale by the automatic signature verification system based on the hidden signature.
EN
The paper presents a comprehensive review of learning methods used to solve various problems in ad-hoc networks. The learning methods are classified according to learning mechanisms and problems solved. Nine representative approaches are discussed in more detail.
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