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Online signature verification by continuous wavelet transformation of speed signals

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014731" target="_blank" >RIV/62690094:18450/18:50014731 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1016/j.eswa.2018.03.023" target="_blank" >http://dx.doi.org/10.1016/j.eswa.2018.03.023</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.eswa.2018.03.023" target="_blank" >10.1016/j.eswa.2018.03.023</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Online signature verification by continuous wavelet transformation of speed signals

  • Original language description

    Despite the imitability of the signatures due to presence of numerous image processing programs, online verification systems could provide sufficient security for e-signatures. Recent developments in touch screen technology and android programming also lead to utilization of hidden interfaces stealthily collecting the unique characteristics and storing the key features aside from geometrics. Therefore, we initially designed a signing interface for touchscreens which stealthily collects the precise coordinates while an individual is signing on the screen by fingertips. Even if the coordinate data is extracted as a matrix consisting of x and y values with corresponding time, the speed array is consequently calculated to investigate the higher frequency regions. The speed data processed by continuous wavelet transformations (CWT) to reveal the frequency information of the signing speed with respect to time information. The grayscale spectrograms created by wavelet transforms are converted into arrays for subsequent training session performed by support vector machines (SVM). The trained network successfully classified further attempts of the real and fake signatures with 1.67% false negative (FNR), 3.33% false positive rates (FPR) and 3.41% equal error rate (EER) for 120 signatures, even though the signature is totally public. For understanding the validity of the CWT and SVM running consecutively, the experiments are re-conducted for the signatures taken from SVC2004 and SUSIG public databases. (C) 2018 Elsevier Ltd. All rights reserved.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Expert systems with applications

  • ISSN

    0957-4174

  • e-ISSN

  • Volume of the periodical

    104

  • Issue of the periodical within the volume

    AUGUST

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    10

  • Pages from-to

    33-42

  • UT code for WoS article

    000434239800003

  • EID of the result in the Scopus database

    2-s2.0-85044146529